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Smart Pattern Recognition

A. Alfalou,1,a C. Brosseau,2 and M. S. Alam3

1ISEN Brest, Groupe Vision, L@bISEN, 20 rue Cuirassé Bretagne, CS 42807, 29228 Brest Cedex 2, France

aEmail: ayman.al-falou@isen.fr 2Université de Brest, Lab-STICC, CS 93837, 6 avenue Le Gorgeu,

29238 BrestCedex 3, France 3Department of Electrical and Computer Engineering, University of South Alabama, 150 Jaguar Dr.,

SH 4122, Mobile, AL36688-0002, USA

ABSTRACT

The purpose of this paper is to test correlation methods for pattern recognition applications. A broad overview of the main correlation architectures is first given. Many correlation data are compared with those obtained from standard pattern recognition methods. We used our simulations to predict improved decisional performance from correlation methods. More specifically, we are focused on the POF filter and composite filter family. We present an optimized composite correlation filter, called asymmetric segmented phase-only filter (ASPOF) for mobile target recognition applications. The main objective is to find a compromise between the number of references to be merged in the correlation filter and the time needed for making a decision. We suggest an all-numerical implementation of a VanderLugt (VLC) type composite filter. The aim of this all-numerical implementation is to take advantage of the benefits of the correlation methods and make the correlator easily reconfigurable for various scenarios. The use of numerical implementation of the optical Fourier transform improves the decisional performance of the correlator. Further, it renders the correlator less sensitive to the saturation phenomenon caused by the increased number of references used for fabricating the composite filter. Different tests are presented making use of the peak-to-correlation energy criterion and ROC curves. These tests confirm the validity ofour technique. Elderly fall detection and underwater mine detection are two applications which are considered for illustrating the benefits of our approach. The present work is motivated by the need for detailed discussions of the choice of the correlation architecture for these specific applications, pre-processing in the input plane and post processing in the output plane techniques for such analysis.

Keywords: correlation, VanderLugt correlator, fast Fourier transform, correlation filter, phase-only filter, asymmetric segmented phase-only filter, face recognition, elderly fall detection, underwater mine detection.

1. INTRODUCTION Determining the degree of similarity between a target image (image to be recognized) and a reference image from a database is a primary goal in the field of pattern recognition [1-7]. Two important architectures implementing correlation are the joint transform correlator (JTC) and VLC. They are generally performed using an optical set-up called 4f. The 4f set-up is an optical system composed of two convergent lenses. The 2D object is illuminated by a monochromatic wave. A first lens performs the Fourier transform (FT) of the input object in its image focal plane. In this focal plane, a specific filteris positioned. Next, a second convergent lens performs the inverse Fourier transform (IFT) in the output plane of the system to get the filtered image. The main difference between JTC and VLC lies in the treatment of the input and output planes. In the following, our discussion will be mainly focused on VLC. This is justified by its simplicity compared to other algorithms [8-13], its global treatment of the target image, its good decisional performances [14-17], and its ability to detect, localize and identify simultaneously a target image within a scene in sharp contrast to other numerical methods, e.g. Viola-Jones face detection algorithm [17-18].

Great progress has been made in the field of optical correlation. Despite this significant progress, the number of papers dealing with that subject slowed down in the recent years. The reasons are threefold. Firstly, many efforts have concentrated on suggesting and validating new correlation filters [1,5]. These efforts concerned essentially treatments in

Invited Paper

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the Fourier plane. However, the use of specific treatments of the input and correlation planes permits to increase significantly the correlator’s performances. In [15], we suggested and validated a denoising method of the correlation plane. This method has beenfound to have significantly bettercorrelation discrimination capability [15]. In [19], another type of treatment of the input plane was able to provide better decisional performances of the correlator [19]. Secondly, a large part of the community working on correlation has focused on all-optical implementation [1-7]. However, even if these all-optical implementations allow having in principle large cadencethey are rather complex to develop [6]. In addition, all-optical approaches have their drawbacks and stringent requirements, i.e. aberration effects, alignment of components, limitation of the overall speed by how fast the information can be updated on the input and output devices, and need of a costly optoelectronic interface. Furthermore, use of all-optics cannot be justified for many applications, especially when the target image size is small. Thus, alternate hybrid (numerical-optical using optoelectronic interface) methods have been proposed as substitutes for all-optical techniques. Moreover, recent advances in reprogrammabletarget such as the graphics processor unit (GPU) [20], or the field-programmable gate array (FPGA) [21] made it possible to manipulate efficiently computer graphics and process large blocks of data rapidly. Our recent results [20] indicate that the GPU implementation of an all-numerical correlator leads to true recognition rates larger than 85 % with a run time lower than 120 ms using fixed images and true recognition rates larger than 77 % using a real video sequence with a run time smaller than 2 frames per second and a database of 100 persons. Thirdly, it is our opinion that the correlation operation should be conceived as part of a global system for making a decision. Within this context, correlation should be combined with a data fusion technique [22]. For example, when the target object is moving it is necessary to make a decision based on correlation features of many images from a video sequence [23].

It is quite clear that using successfully correlation methods requires good knowledge of the application and basic

requirements such as kind of target images, expected levels of decision robustness and discrimination, real time processing, lightning conditions, noise, etc. Our results point that this set of parameters will impose the correlation architecture and the most appropriate type of correlation filter for the application considered. Consider, for example, the target images Lena et Barbara shown in Fig.1(a) and Fig.1(b), respectively. Their respective FTs lead to significantly different spectra. While Lena’s spectrum is almost centered (Fig. 1 (c)), Barbara’s spectrum shows a larger spatial distribution in the spectral plane. The above mentioned issues are extremely relevant here because the choice of the correlation filter will impact correlator’s performances. For a face recognition application, the correlation filter should be able to deal with face rotation with respect to the reference image [24-25]. Specially designed correlation filters to deal with color information have already demonstrated outstanding performances for smart pattern application[26-28].

Fig. 1: Example of target images: (a) Barbara, (b) Lena, (c) and (d) are the amplitude spectra of Lena and

Barbara, respectively.

The main purpose of this work is to present real-time correlation-based pattern recognition techniques. Emphasis will be placed on the composite filter family in order to deal with speed requirement [29-31].Composite filters allow merging different reference images, thus reducing the number of filters to be used for obtaining a reliable decision. More specifically, we consider in detail a new composite filter called asymmetric segmented phase-only correlation filter (ASPOF) allowing us to increase significantly the number of reference images in the fabrication of this filter [32]. We highlight the saturation issue (number of bits required for coding the correlation filter’s pixels). Whatever the type (optical, numerical, or hybrid) of implementation chosen for the correlator, a severe limitation of this method lies in this bit number. For example, all-optical approaches, however, are not the universal panacea and have their drawbacks and stringent requirements, i.e. aberration effects, alignment of components, limitation of the overall speed by how fast the

information can be updated on the input and output devices, and need of a costly optoelectronic interface [33]. On the other hand, even if all-numerical implementations lead to a larger applicability, reconfigurability, they require large memory space and computational time depending on the type of correlation filter used [21].

The present work is motivated by a need to present two important correlation applications: underwater mine detection and recognition [34] and patient monitoring application in home video surveillance [23]. It is also motivated by the need for detailed discussions of the choice of the correlation architecture for these specific applications, pre-processing in the input plane and post processing in the output plane techniques for such analysis. Indeed, the first application requires denoising target images since they have small contrast with backscattering and lightning complications, while in the second applicationour algorithm performs a fast 3D recovery of the subject’s head position, involves a model based prediction to guide the tracking procedure, and also involves an adapted fuzzy logic control algorithm to make decision based on information given to the system.

The rest of the paperis organized as follows. We first recall the basic principle of VLC and discuss the

characteristics of standard correlation filters with the general purpose of helping the use for optimizing its choice against saturation, decision criterion, set of reference images, robustness, and discrimination. Other types of filterswerediscussed in Refs. [1,5,14,32].

2. VANDERLUGTCORRELATOR

Fig. 2 shows the generic VLC operation. It is described by three planes: the input (target image) plane, the Fourier plane (spectrum of the target image multiplied by the correlation filter fabricated from reference images of a database), and the correlation (output) plane. Passing from one plane to another is realized via a Fourier transform (FT).

Fig. 2: Synoptic diagram of the VLC.

Several important correlation filters are now briefly discussed.

(1) Adaptive filter ( adaptiveH ) [37]. This is the standard filter used by Vanderlugt. It reads

( ) ( )( )

adaptive

I u vH u v

B u vα= , Eq. (1)

where α denotes a constant, (u, v) are spectral coordinates, ( )* ,I μ ν is the complex conjugate of the spectrum of the

reference imageR, and ( ),B u v represents the spectral density of the input noise.The drawback of this filter is that it

leads to broad correlation peaks in the correlation plane. Since the output plane is scanned for this peak, and its location indicates the position of the target in the input scene, we can conclude that the target is poorly localized. In addition, its discriminating ability is weak.

Input planeFourier plane

Fourier plane

CorrelationFilter

FT IFT

FT: Fourier Transform IFT: Inverse Fourier Transform

(2) Phase-only filter (POF) ( POFH ) [38]. Using computer simulation Horner and Gianino [38] have proposed a

pure phase filter for achieving improved correlation discrimination.

( ) ( )( )

I u vH u v

I u v= . Eq. (2)

( )vuI , is the amplitude of the reference’s spectrum which is chosen for fabricating the correlation filter. It is worthy to

note that Eq. (2) depends only the phase of the reference. Besides the ability to get very narrow correlation peaks, POF have another feature that adapted filters lack: the capacity for discriminating objects. Because POF use only the reference’s phase, they can be useful as edge detector. However, as is well known the POF is very sensitive to even small changes in rotation, scale and noise contained in the target images. We consider also the binarized version of the phase-only filter [6], or alternatively defined as a two-phase filter where the only allowed values are 1 and -1 such as

1BPOFH = if the real part of POF filter 0≥ , and 1BPOFH = − otherwise. Generally, BPOF have weaker performances

than POF. It is helpful in certain applications for which the size of the filter should be small and also for optical implementation. Like POF, BPOF is very sensitive to rotation, scale, and noise in the target images.

(3) Inverse filter ( inverseH ) [40] defined as the ratio of the complex conjugate of the reference image’s spectrum R

by the magnitude of the reference image spectrum, and can be expressed as

( ) ( )( )

Rinverse

I u vH u v

I u v= Eq. (3)

The main advantage of this filter is to minimize the correlation peak width. It has the desirable property of being very discriminating. But, it is very sensitive to deformation and noise contained in the target image with respect to the reference image.

(4) Composite filter ( compositeH ) [1,5,41]. The basic idea consists in merging several references by linearly

combining them and then fabricating the corresponding POF such as

( ) ( ) ( ) ( )( ) ( )( )

, , , ,R R Rn

composite R R Rn

R R Rn

I I IH u v POF I u v I u v I u v

+ += + + =

Eq. (4) Once the correlation filter is fabricated, it is multiplied by the target image’s spectrum (Fig. 2). Then, proceeding with an inverse Fourier transform (IFT) we obtain a correlation plane. Such correlation plane is characterized by a correlation peak when there is a strong similarity between the target and reference images, i.e. Fig. 3(a) and Fig. 3(c) corresponding to target and reference images (Lena). Otherwise, the correlation is more or less flat, i.e. Fig. 3 (b) and Fig. 3 (d) corresponding to target image(Lena) and reference image(Barbara). Note that Fig. 3 (a) and Fig. 3 (b) were obtained by using an adapted filter while Fig. 3 (c) and Fig. 3 (d) were obtained with a POF.

2.1. Decisioncriterion

To make a decision, the correlation peak should be characterized precisely (shape, height, etc). These metrics will determine the degree of similarity between the target and reference images. Several discrimination factors are used in the literature, e.g. the peak-to-correlation energy (PCE) [42-43]). The PCE is given by the ratio of the energy in the correlation peak to the overall energy of the correlation plane. The PCE values are in the range [0,1] ; values close to the lower bound corresponds to the absence of any resemblance between the target and reference images (Fig. 3(b) and Fig. 3(d)), while values close to the upper bound corresponds to perfect match between the target and reference images (Fig.

3(a) and Fig. 3(c)). To illustrate the usefulness of this metric to compare the performances of correlation filters, we show two examples of correlation results for a specific image with, on the one hand, a high and narrow correlation peak obtained with the POF (Fig. 3(a), and on the other hand, a broader correlation peak than that of the POF (Fig. 3(c)). Both peak and energies are measurable quantities. In what follows, we define the correlation peak over 3 × 3 pixels.

2.2. Imagedatabase In this work we consider images from the Pointing Head Pose Image Database (PHPID) [44]. Two subjects are selected: P1 in Fig 4(a) and Fig. 4(b),and P2 (Fig. 4(c)).

Fig.3:Examples of correlation results : (a) autocorrelation plane with an adapted filter, (b) correlation plane with an adapted filter for a target image different than the reference image. (c) autocorrelation plane with an adapted

filter with a POF, (d) correlation plane with a POF for a target image different than the reference image.(e) Definition of the correlation peak over 3× 3 pixels.

As shown in Fig. 4(a) and Fig. 4(b) vertical (0° and +10°) and horizontal (-90° to 90 °) of subject P1 are considered. The series with +10° rotation will be used for testing the correlator’srobustness. In Fig. 4(c) the subject P2 is considered without any vertical rotation. This series will be eventually used for studying the correlator’s discrimination. The face of P1 which is framed in red in Fig. 4(a) is used for fabricating the correlation filter.

Fig.4: Faces used for testing the robustness and discrimination of the correlator: (a) subject P1 with vertical rotation set to 0° and horizontal rotation ranging from -90° to +90°. (b) Same as in (a) P1 with a vertical rotation

set to +10° .(c)faces corresponding to subject P2.

(a) (b) (d)(c)

2A 10+

208 10 12

2.3. ROC curves

Previously we considered correlation discrimination quality based on the PCE value characterizing the correlation plane. It can be argued that this discrimination quality can be also determined by a ROC curve, which is a graphical plot of the sensitivity, or true positive rate (TPR), versus false positive (1-specificity or 1-true negative rate (TNR)) [45]. Each point of the ROC curve is obtained from the correlation results of the entire base with a given filter and a prescribed threshold for comparing PCE values.

3. CORRELATION PARAMETERS

3.1. Choosing a correlation filter and its influence on robustness and discrimination As was mentioned above, we focus our attention on VLC architecture for a face recognition application. The tests have been performed for three correlation filters and PHPID. (1) Adaptivefilter Fig. 5(a) shows the correlation planes obtained by correlating the adapted filter fabricated with the reference image of subject P1 framed in red) with the complete database of P1. For the simulations with all faces of P1, the correlation peaks are characterized by PCE which are displayed in the green curve of Fig.5(b) where the abscissa indicates the number of faces of P1 and P2 used. The PCE values corresponding to correlation with faces of subject P1 depicted in Fig. 4(b) are shown in blue (Fig.5(b)). The PCE values corresponding to the correlation with faces of subject P2 displayed in Fig.4(c) are shown in red (Fig.5(b)).

Fig. 5. (a) Correlation planes of subject P1 obtained with an adapted filter fabricated with face of P1 framed in red (Fig. 4(a)). (b) Corresponding PCE values: the green curve corresponds to the correlation of all faces of P1, the blue curve corresponds to the correlation of P1 (rotation +10° in Fig. 4(b)), and the red curve corresponds to

the correlation with faces of P2 (Fig.4(c)).

In order to study the robustness and discrimination ability of the correlator, it is convenient to define the robustness as the difference between the maximum value of the PCEs (corresponding to autocorrelation) and the minimum value of the PCEs for every images of P1. That is

(a) (b)

( ) ( )

green curve green curveR max PCE min PCE

= −, Eq. (5)

where 1R (resp. 2R ) and denotes the maximum (resp. minimum) value obtained with every images of P1. Within this

definition a robust correlator is characterized by 0R = . The discrimination is introduced as the difference between the minimum PCE value obtained with every images of P1 and the maximum value obtained with images of P2. A highly discriminating correlator should be characterized by a positive and large value of D .

( ) ( )

green curve red curveD min PCE max PCE

= −, Eq. (6)

Based on these definitions, it is seen that the correlator which makes use of an adapted filter is quite robust, i.e. 0.15R ≅ , but not discriminating, i.e. 0.1D ≅ − . Now, the POF-based correlation is presented as a solution for overcoming this

discrimination limitation. (2) POF The POF is fabricated from the reference image of P1framed in red color. The correlation results are shown in Fig. 6 for all faces of P1 and P2. There are a few matters that must be noted about the discrimination ability of the POF from the correlation planes displayed in Fig 6(a), Fig. 6(b), and Fig. 6(c). Firstly, the green curve corresponding to correlation of subject P1 (Fig. 4(a)) is always greater than other curves for every set of images. Likewise, the blue curve which corresponds to the correlation of subject P1 (Fig.4(b)) is close to the red curve dealing with correlation of subject P2 (Fig.4(c)). Secondly, the POF’s robustness is weak since the PCE values of the green curve become very small as one considers different images than the reference (central) image. The ROC curves are summarized in Fig. 6(e). We have also performed several tests with different threshold values of the PCE graphs shown in Fig. 6(d). For each threshold, the TPR and false positive rate (FPR) were calculated. Each point of the ROC curve (Fig. 6(e)) corresponds to a fixed threshold. We found a TPR of 40% for 0% of FPR. The largest value of TPR is 66% for a TPR of 15%. These tests show that the correlation results are better than those obtained for the adapted filter; however, they are not very good. (3)BPOF and inverse filter Calculations were performed to test the BPOF. As mentioned above, one of the main advantagesof BPOF is that it is compatible with optoelectronic interfaces. As shown in Fig. 6(a) and Fig. 6(b), the performances of the BPOF are weak compared to those of the OF. Results for the inverse filter are displayed in Fig.7(c) and Fig. 7(d). The inverse filter has better discrimination performances than the BPOF.

a e a°aa

(b) (c)

(d) (e)

Fig. 6 : correlation results for a POF (a) correlation of P1(no horizontal rotation), (b) correlation of P1 (rotation +10° in Fig 4(b)) , (c) correlation with subject P2 (d) corresponding PCE values (e) corresponding ROC curve.

0 2 4 6 8 10 12 140

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

2 4 6 8 10 12 14

01 02 03 04 05 06FPR

DD + 2 t t 4 t t t t t t

fi 8 10 12

0 i i i i

0 0.1 02 0.3 0.4 05 06 07 08 09FPR

(a) (b)

Fig. 7: (a) and (b) correspond to correlation results for a BPOF; (c) and (d) correspond to correlation results for an inverse filter.

These various tests show that the first parameter to do correlation is the type of filter. Selecting the filter with the better robustness and discrimination performances requires special attention. Robustness will determine the number of references required for the specific application considered. The more robust is the filter the less reference images are required.

3.2. CORRELATION PLANE AND NONLINEARITY

We investigated the influence of a non-linearity in the correlation plane before applying the criterion decision. We present the results of the post-processing in Fig. 8 using a POF and setting the power of the “1” to 10. Fig 8 (a) and fig. 8 (b) present results without correlation plane post-processing (nonlinearity is set to 1). Fig.8(c) and Fig. 8(d) indicate that such post-processing increases significantly the making of decision (of nonlinearity is set to 2). Here TPR is equal to 62% for FPR set to 0%. However, we established that in some cases this nonlinear operation can be detrimental to decision as shown in Fig. 8(d) and Fig.8(e) when the degree of nonlinearity is set to 10. Even if the POF’s robustness is better, the tests indicate that the

Ff os.

01 0? 03 04 os os or os os 1

correlation results are not discriminating. These various tests show that care should be exercised when using this nonlinearity trick, and that the degree of nonlinearity should be less than 3.

Fig. 8.Introducing a nonlinearity in the correlation plane

3.3. NUMBER OF REFERENCES VERSUS NUMBER OF FILTERS

The basic correlation operation indicating a match between two patterns, i.e. target and reference images, involves a large number of comparisons to make a reliable decision. This results from the fact that the target image may have a large number of variants. To circumvent this difficulty, we have reformulated our analysis using composite filters [1,5,29]. The basic principle is to fabricate a single filter with several reference images, i.e. Eq. 4. This has for immediate

0 2 4 6 8 10 12 140

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

Proc. of SPIE Vol. 8748 874809-10

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Proc. of SPIE Vol. 8748 874809-11

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Let us now fabricate a 10-reference filter for the purpose of recognizing all faces of P1 (shown in Fig. 4(a) and Fig. 4(b)). The correlation results, depicted in Fig. 9(c) and Fig. 9(d), show that a single filter allows us to recognize subject P1. Remarkably, these results make apparent that composite filters are effective for recognizing patterns. However, with increasing the number of references care should be exercised to the saturation issue that will be discussed below.

3.4. INFLUENCE OF THE NUMBER OF PIXELS FOR CALCULATING THE PCE We start by recalling that the PCE is given by the ratio of the energy in the correlation peak to the overall energy of the correlation plane. Since the correlation peak cannot be defined over a single pixel, it necessitates defining correlation peak area in order to quantify the PCE. Thus, PCE can be defined as the ratio of the correlation peak area to the overall energy of the correlation plane. We are now focusing the discussion on the influence of the correlation peak area on the correlation results obtained with a 5-reference POF (Fig. 10). Loosely speaking, there is a clear relationship between increasing the correlation peak area and the correlator’s performances: it has for effect to increase the correlator’s robustness but to decrease its discrimination ability.

(d) Fig. 10 : Correlation obtained with a five-reference composite filter and a correlation peak area defined over (a)

( )1 1 pixels× , (b) ( )3 3 pixels× , (c) ( )7 7 pixels× , and (d) ( )11 11 pixels× .

Proc. of SPIE Vol. 8748 874809-12

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tO 12 14

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3.5. SATURATION We are now in a position to carry out an analysis of the saturation issue. This is an important issue that arises because of the limitations of the optoelectronic interfaces. Another crucial parameter is the number of bits that is used to encode each pixel, i.e. the filter’s size. Table 1 illustrates several results dealing with the saturation issue. The second table of Table 1 presents the correlation results without saturation, while the third column shows the corresponding results obtained by limiting the number of bits for encoding the correlation filter, i.e. 4 bits for the POF, 4 bits for the inverse filter, 9 bits for the 7-reference composite filter, and 10 bits for the 10-reference composite filter. Type de filtre Sans saturation Avec saturation

(filtre codé sur 4 bits)

Inverse

(filtre codé sur 4 bits) Composite 7 Références

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002 4 6 10 12 14

Composite 10 Références

Table 1: A comparison of the correlator’s performance against the saturation effect.

3.6. RECOMMANDATIONS Overall, some remarkable results can be obtained by using correlation methods for pattern recognition if the filter’s choice is relevant to the application considered [1]. This choice will impact the filter’s robustness and discrimination. Including a specific post-processing treatment, such as the nonlinearity considered in section 3.2, can increase significantly the correlator’s performance. See also Ref. [15] for alternative treatment based on the denoising the correlation plane before applying the PCE criterion. A linear functional model can be employed to express a given correlation plane as a linear combination of the correlation peak, noise and residual components. In this analysis, the correlation peak is modeled using an orthonormal function and the singular value decomposition method. A set of training correlation planes is then selected to create the correlation noise components. Finally an optimized correlation plane is reconstructed while discarding the noise components. Independently of the filter correlation used, this technique denoises the correlation plane by lowering the correlation noise magnitude in case of true correlation and decreases the false alarm rate when the target image does not belong to the desired class. Test results were presented in [15], using a composite filter and a face recognition application, to verify the effectiveness of the proposed technique.

4. ASPOF

A new type of optimized composite filter, i.e. the asymmetric segmented phase-only filter (ASPOF), was proposed for improving the effectiveness of the VLC when used for face identification [32]. Basically, it consists in merging several reference images after application of a specific spectral optimization method. After segmentation of the spectral filter plane to several areas, each area is assigned to a single winner reference according to an optimized criterion. The reference image base is separated in two sub-classes ((1) and (2)) in Fig. 11.

Two segmented filters are constructed for the two sub-classes. Selection and assignment optimized criterion used

for segmentation of the Fourier plane for both filters are realized according to a segmentation criterion: the pixel of the filter is assigned to a given reference if and only if its energy relative to spectral position is larger than the energies of all other references.Otherwise, it is not assigned. Unclassified pixels are assigned to one of the two spectra by looking at their closest neighbors in order to avoid isolated pixels. An isolated pixel represents a pixel of a spectrum l which is surrounded by pixels of the spectrum k. Isolated pixels are detrimental to the segmented filter’s performance; the effect being more and more important as the number of references which define the filter is increased.

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References base

RR2-3 a-

References Sub-base (1) References Sub-base (2)

IHSPOF

Asymmetric Segmented composite Ph ase Only Filter"ASPOF"

Many tests dealing with binary and gray scale images identifications [32] showed that this method offers a significant performance improvement on standard composite filters for face identification. Use of this filter has for effect to increase the recognition rate and to decrease the false alarm rate.

Fig. 11.Principle of asymmetric segmented composite phase only filter(ASPOF) [32].

5. APPLICATIONS

Before considering several concrete applications of the approaches described above we argue below how to answer this important question optical or numerical implementation of the correlation operation?

5.1. OPTICAL VERSUS NUMERICAL CORRELATION

An example of an all-optical approach of correlation is depicted in Fig.12 [6]. Ouerhaniet al. [20] have developed an all-numerical correlator for face recognition applications which utilizes the computational resources of a graphics processing unit (GPU) to accelerate the computation-intensive part of a face recognition algorithm based on VLC. They presented and validated an optimization protocol of a digital correlator by suggesting a two-level decision tree learning approach: a first level is considered to class the target image and a second level is used for identification. Recognition rate larger than 85 %with a run time lower than 120 ms have been observed for test samples from PHPID, and implementation of the digital correlator usingGeForce 8400 GS processor obtained from NVIDIA. IN [27], we compared the performances of a numerical correlator based on the fast FT (FFT) with that relying on a simulation of the Fraunhofer diffraction. Different tests using the PHPID and considering faces with vertical and horizontal rotations were performed with the code MATLAB. Tests were conducted with a 5-reference optimized composite filter. The ROC curves show that the optical FT simulating the Fraunhofer diffraction leads to better performances than the FFT. This correlation method can be implemented using an all-numerical and reprogrammabletarget such as the GPU, or the field-programmable gate array

Asymmetric segmented composite filter ASPOF

(b) (a)

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d also ascertaicolor image (Fis less sensitivnd V represenning condition

n the image is

in if they haveFig. 13(a)) intove to lightningntations of thisn.

e o g s

Proc. of SPIE Vol. 8748 874809-16

Fig The H and Scontours of tThis is done b

Detection of tshown in FigConsequentlymuffins in the

Fig. 15 : Co

. 13: (a) Scen

S components the muffins arby filling the d

the muffins isg. 15(a). Nexy, it has been e image.

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etermine the p

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positions of al

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s ). ll

Proc. of SPIE Vol. 8748 874809-17

Our approach reliably detect the presence of non-perfect muffins having non-circular shapes (Fig. 16(a) and Fig. 16(b)). This is in sharp contrast with the results obtained with an adapted filter (Fig. 16(c) and Fig. 16(d)). Sensitivity can be improved by using inverse filters [1] filter, or AMPFs [28].

Fig. 16: (a) Picture of muffins and (b) its correlation with a POF. (c) and(d) the correlation obtained with an

Adapted filter.

5.3. FACE RECOGNITION

Anotherapplication considered herein involves face recognition using a laptop’s webcam. The first step is done by fabricating a set of reference images. Then, a composite filter is chosen for this application. As the laptop is switched on, the camera takes a picture and the correlation is realized for identification of the subject. If the PCE value is larger than the specified threshold the subject is identified as the person authorized to use the laptop. Figure 17(a) shows an example of the target image while Fig. 17(b) considers a reference image. Note that in Fig. 17(b), the face is surrounded by a black area for the purpose of avoiding noise which can trigger false alarms.

(a) (b)

Fig.17: (a) Example of a target image, (b) corresponding reference image.

Damaged Cakes

Proc. of SPIE Vol. 8748 874809-18

We find again

5.4. FU

Home automtheir indepenvarious postudecisions on Secondly, wegoals of accusystem permibe incorporat

Our algorithmoptical correlsystem also i

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niques to make environmentfocused on thee subject. Thes of alarm can

prediction andlor space. Then given to the

n s e t. e e n

Proc. of SPIE Vol. 8748 874809-19

0á, 0.4

nomine no

presence mine

minemine

presenceno

600 800images

system. The different experiments demonstrated the good performances of our approach. For the four scenarios considered in this study, the level of alarm is close to 89%. The technique presented does appear to possess good performances for identifying and tracking a subject as he or she enters the room, and to analyze various posture-based events including normal daily life activities, and unusual events, e.g. fall detection. The prototype can be tested for every subject for whom a given base of reference images exists.

5.5. MINE DETECTION AND RECOGNITION [34] This application concerns underwater mine detection using the JTC architecture [34]. A pre-processing treatment of the images is necessary due to their low quality (Fig. 19). We optimized a fringe-adjusted JTC by including nonlinearity in the Fourier plane [35]. As shown in Fig. 20 this method is well adapted to mine detection. In addition, use of polarized light to denoise the input plane [46] allowed us to enhance significantly the correlator’s performance.

Fig. 19:Three examples of target images showing different types of mine.

Fig. 20: Results of the optimized nonlinear fringe-adjusted JTC, k=0.85 [34].

Proc. of SPIE Vol. 8748 874809-20

6. SUMMARY In conclusion, the present work has provided a broad presentation of correlation methods for smart pattern recognition. A nuts and bolts implementation of correlation methods was proposed to get optimized decisional performances. It is hoped that the various tests presented here form some basis for understandingcorrelation analysis for the purpose of pattern recognition.

7. ACKNOWLEDGMENTS Work in L@bISEN was supported by Groupe-ISEN. The technical assistance of N5_ISEN student is gratefully acknowledged.

REFERENCES

[1] A. Alfalou and C. Brosseau, "Understanding Correlation Techniques for Face Recognition: From Basics to Applications," Face Recognition Book, MilosOravec (Ed.), ISBN: 978-953-307-060-5, INTECH, 354-380 (2010). http://sciyo.com/articles/show/title/understanding-correlation-techniques-for-face-recognition-from-basics-to-applications [2] P. K. Banerjee, A. K. Datta, “Techniques of Frequency Domain Correlation for Face Recognition and its Photonic Implementation”, AdamoQuaglia (Editor) and Calogera M. Epifano (Editor), Chap 9, 165-186 (2012). ISBN: 978-1-61942-663-4; https://www.novapublishers.com/catalog/product_info.php?products_id=28370 [3] F.T.S. Yu and S. Jutamulia, “Optical Pattern Recognition” CambridgeUniversity Press (1998) [4] M. S. Alam, Ed., Optical Pattern Recognition Using Joint Transform Correlation, 687 pages, SPIE Milestone Series, SPIE Press, Bellingham, WA, August 1999. [5] I. Leonard, A. Alfalou and C. Brosseau, “Face recognition based on composite correlation filters: analysis of their performances” in Face Recognition: Methods, Applications and Technology, Adamo Quaglia (Editor) and Calogera M. Epifano (Editor), Chap 3, 57-80 (2012). ISBN: 978-1-61942-663-4. https://www.novapublishers.com/catalog/product_info.php?products_id=28370 [6] A. Alfalou, “Implementation of Optical Multichannel Correlators: Application to Pattern Recognition,” Ph.D. Thesis, Université de Rennes 1–ENST Bretagne, France (1999). [7] M. S. Alam, “Optical Pattern Recognition,” in Encyclopedia of Optical Engineering, (Marcel Dekker, New York), pp. 1778-1797 (2013). [8] Y. Zhang, J. Tian, X. He and X. Yang, “MQI Based Face Recognition Under Uneven Illumination,” Advances in Biometrics, vol. 4642, pp. 290-298, (2007). [9] G. An, J. Wu and Q. Ruan, “Kernel TV-Based Quotient Image Employing Gabor Analysis and Its Application to Face Recognition,” IEICE Transactions on Information and Systems, vol. E91-D, no. 5, pp. 1573-1576 (2008). [10] S. G. Bhele, V. H. Mankar,“A Review Paper on Face Recognition Techniques,“International Journal of Advanced Research in Computer Engineering & technology Vol. 1, No 8 (2012). [11] H. Zhang and Y. Zhang and T. S. Huan, ”Pose-robust face recognition via sparse representation,” Pattern Recognition, In press (2012). [12] H. Zhang, N. M. Nasrabadi, Y. Zhang and T. S. Huang, “Joint dynamic sparse representation for multi-view face recognition, Vol. 45, Issue 4, Pages 1290–1298 (2012). [13] D. Chrysostomou, A. Gasteratos, L. Nalpantidis and G. C. Sirakoulis, “Multi-view 3D scene reconstruction using ant colony optimization techniques,” Meas. Sci. Technol., 23, 114002 (2012). doi:10.1088/0957-0233/23/11/114002 [14] P. Katz, A. Alfalou, C. Brosseau, M. S. Alam, “Correlation and Independent Component Analysis Based Approaches for Biometric Recognition ,” in Face Recognition: Methods, Applications and Technology, Adamo Quaglia (Editor) and Calogera M. Epifano (Editor), Chap 11, 201-229 (2012). ISBN: 978-1-61942-663-4 https://www.novapublishers.com/catalog/product_info.php?products_id=28370 [15] A. Alfalou, C. Brosseau, P. Katz, and M. S. Alam, "Decision optimization for face recognition based on an alternate correlation plane quantification metric," Opt. Lett.37, 1562-1564 (2012).

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[16] A. Alsamman and M. S. Alam, "Comparative study of face recognition techniques that use joint transform correlation and principal component analysis," Appl. Opt. 44, 688-692 (2005). http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-44-5-688 [17] P. Viola, M. Jones et D. Snow,”Detecting Pedestrians using Patterns of Motion and Appearance”, International Journal of Computer Vision, vol. 63, no 2, 153-161 (2005). [18] H. Jia, Y. Zhang, W. Wang, J. Xu,“Accelerating Viola-Jones Face Detection Algorithm on GPUs”, High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), 2012 IEEE 14th International Conference, Page(s): 396 – 403 (2012). DOI.10.1109/HPCC.2012.60 [19] A. Alfalou, M. Elbouz, Ali Mansour, and G. Keryer, "New spectral image compression method based on an optimal phase coding and the RMS duration principle,” J. Opt., 12, 115403 (12 pp) (2010). [20]Y. Ouerhani, M. Jridi, A. Alfalou, and C. Brosseau, ”Graphics Processor Unit Implementation of Correlation Technique using a Segmented Phase Only Composite Filter,” Opt.Commun.289, 33–44 (2013). [21] Y. Ouerhani, M. Jridi and A. Alfalou, “Area-Delay Efficient Architecture Using Parallel Processing and New Memory Sharing Technique,” Journal of Circuits Systems and Computers, 21, 1240018-1-1240018-15 (2012). [22] S. Hou, S. Zhouand M. A. Siddique "Query by example video based on fuzzy c-means initialized by fixed clustering center", Opt. Eng. 51(4), 047405 (2012); http://dx.doi.org/10.1117/1.OE.51.4.047405 [23] M.Elbouz, A. Alfalou and C. Brosseau, "Fuzzy logic and optical correlation-based face recognition method for patient monitoring application in home video surveillance," Opt. Eng. 50, 067003 (2011). http://dx.doi.org/10.1117/1.3582861 [24] K. M. Iftekharuddin, F. Ahmad, and M. A. Karim, "Rotation invariant target recognition using amplitudecoupled minimum average correlation energy filter", Opt. Eng. 35, 1009-1014 (1996). [25] M. S. Alam and.A. A. S. Awwal, “Scale invariant amplitude modulated phase-only filtering,” Opt. Laser Technol. 32, 231-234, 2000. [26] M. Nazrul Islam, M. S. Alam, and M. RafiqulHaider, "Class-associative color pattern recognition using ashifted phase-encoded joint transform correlation", Opt. Eng.45, 075006 (2006); http://dx.doi.org/10.1117/1.2227364 [27] M. Elbouz, A. Alfalou, C. Brosseau, M.S. Alam, S. Qasmi, Y. Ourhani, “Sensitivity of optical correlation to color change of target images,” Proc. SPIE 8398 (2011), Optical Pattern Recognition XXIII, 83980A; doi:10.1117/12.919768. [28]A. A. S. Awwal, “What can we learn from the shape of a correlation peak for position estimation?” Appl. Opt. 49, B40-B50 (2010). [29] B. V. K. Vijaya Kumar, “Tutorial survey of composite filter designs for optical correlators,” Appl. Opt. 31, 4773-4801 (1992). [30] V. H. Diaz-Ramirez, “Constrained composite filter for intraclass distortion invariant object recognition,” Optics and Lasers in Engineering 48, 1153-1160 (2010). [31] A. Alfalou, G. Keryer, and J. de Bougrenet de la Tocnaye, "Optical Implementation of Segmented Composite Filtering," Appl. Opt. 38, 6129-6135 (1999). [32] I. Leonard, A. Alfalou, and C. Brosseau, "Spectral optimized asymmetric segmented phase-only correlation filter," Appl. Opt. 51, 2638-2650 (2012). [33] G. Keryer, J. de Bougrenet de la Tocnaye, and A. Alfalou, "Performance comparison of ferroelectric liquid-crystal-technology-based coherent optical multichannel correlators," Appl. Opt. 36, 3043-3055 (1997). [34] I. Leonard, A. Alfalou, M. S. Alam, and A. Arnold-Bos, ”Adaptive nonlinear fringe-adjusted joint transform correlator,” Opt. Eng. 51 (9), 098201-1-098201-14 (2012). doi:10.1117/1.OE.51.9.098201. [35] A. Alfalou, N. Ben HajYahia, M. S. Alam, “Face recognition using a non-zero-order correlation plane and a nonlinear joint transform correlator.”Proc. SPIE, Optical Engineering + Applications, Optics and Photonics for Information Processing V, 8498, 8498-46 (2012). [36] A. A. Sakla, W. A. Sakla, and M. S. Alam, "Hyperspectral target detection via discrete wavelet-based spectral fringe-adjusted joint transform correlation," Appl. Opt. 50, 5545-5554 (2011); http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-50-28-5545 [37] A. VanderLugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theor. 10, 139-145 (1964). [38] J. L. Horner and P. D. Gianino, “Phase-only matched filtering,” Appl. Opt. 23, 812-816 (1984). [39] J.L. Horner, B. Javidi, and J. Wang, “Analysis of the binary phase-only filter,” Opt. Comm. 91, 189–192 (1992).

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[40] R. C. Gonzalez and P. Wintz, “Digital Image Processing,” Addison-Wesley (1987). [41] B. V. K. Vijaya Kumar, “Tutorial survey of composite filter designs for optical correlators,” Appl. Opt. 31, 4773-4801 (1992). [42] B. V. K. Vijaya Kumar and L. Hassebrook, “Performance measures for correlation filters,” Appl. Opt. 29, 2997-3006 (1990). [43] J. L. Horner, "Metrics for assessing pattern-recognition performance," Appl. Opt. 31, 165-166 (1992). [44] N. Gourier, D. Hall, and J. L. Crowley, “Estimating Face Orientation from Robust Detection of Salient Facial Features,” Proc. of Pointing 2004, ICPR, International Workshop on Visual Observation of Deictic Gestures. [45] J. P. Egan, Signal Detection Theory and ROC Analysis (Academic Press Series in Cognition and Perception, New York, 1975). [46] M. Dubreuil, P. Delrot, I. Leonard, A. Alfalou, C. Brosseau and A. Dogariu, "Exploring underwater target detection by imaging polarimetry and correlation techniques," Appl. Opt.52, 997-1005 (2013).

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