cicn 2014 ieee paper

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An Extensive Ssurvey on Feature Extraction Techniques for Facial Image Processing

Vivek PaliDE, RCET,Bhilai(C.G.)

[email protected]

Suchita GoswamiDE, RCET, Bhilai

[email protected]

Lalit P. BhaiyaET&T, RCET, Bhilai

[email protected]

Abstract In this research paper an extensive literature surveyon different types of feature extraction techniques is reported.To provide an extensive survey, we not only categorize existingfeature extraction techniques but also provide detaileddescriptions of representative approaches within each category.These techniques are simply classified into four majorcategories, namely, feature based approach, appearance basedapproach, template-based and part-based approaches. The aimof this paper is to report an illustrative and comparative studyof most popular feature extraction methods which aregenerally used in face recognition problems. This paperprovides an up-to-date comprehensive survey of existing facerecognition researches. We are motivated by the lack of direct

and detailed independent comparisons of all possible algorithmimplementations in available literature. After extensiveresearch on these feature extraction techniques we found thatdifferent feature extraction techniques yield prominent resultsfor different image processing applications.

Keywords-complexity; processing time; feature-based;appearance-based; template-based; part-based; machinerecognition

I. I NTRODUCTION

Face recognition is a task which can be performedremarkably easily and successfully by humans. Thissimplicity was shown to be dangerously misleading as theautomatic face recognition seems to be a problem that is stil l

far from solved. Despite more than thirty years ofcomprehensive research, a number of papers published in journals and conferences related to this field, but we stillcannot declare that artificial intelligent systems can measureto human performance. Face recognition is such achallenging yet interesting problem that it has attractedresearchers with different backgrounds: psychology, patternrecognition, neural network, computer graphics andcomputer vision. The literature on face recognitiontechnology is vast and diverse due to this fact. In this paper,a detailed review of current developments in facerecognition technology is provided. Various algorithms were

proposed and research groups across the world reporteddifferent and often contradictory results when comparingthem. The aim of this survey paper is to provide acomprehensive and comparative study of four most popularcategories of feature extraction techniques for facerecognition systems in completely equal working conditionsregarding preprocessing and algorithm implementation. Weare motivated by the lack of direct and detailed independentcomparisons of all possible algorithm implementations inavailable literature. Features are properties which describethe whole image. It is a crucial piece of information which issubjected to solve the computational task relevant to specific

application [1]. Some significant and useful features fromthe digital image are extracted to be subjected to theselection and classification. The main purpose of the featureextraction is to minimize the original dataset by derivingsome properties which can be used to classify and torecognize patterns that are present in the input images. Afeature vector, resulted from feature extraction process,whose dimension is equal to the number of extracted feature,is derived. These features should retain the importantinformation and should be different enough among classesfor a good classification performance. Consequently, thefeature extraction process plays a decisive role in theclassification performance and thus, in the overallsegmentation process.

II. C LASSIFICATION OF VARIOUS FEATURE EXTRACTIONTECHNIQUES

Feature extraction can be performed using variousmathematical models, image processing techniques andcomputational intelligent tools such as neural networks orfuzzy logic. They are generally classified into fourcategories, namely, feature based, appearance based,template-based and part-based approaches. The first threecategories are frequently discussed in literatures, while theforth category is a new approach employed in recent

computer vision and object recognition. Figure 1 shows theclassification of various feature extraction techniquesemployed in previous research works.

Figure 1: Classification of various feature extraction techniques

2014 Sixth International Conference on Computational Intelligence and Communication Networks

978-1-4799-6929-6/14 $31.00 2014 IEEEDOI 10.1109/.43

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2014 Sixth International Conference on Computational Intelligence and Communication Networks

978-1-4799-6929-6/14 $31.00 2014 IEEEDOI 10.1109/CICN.2014.43

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III. A PPEARANCE -BASED FEATURE EXTRACTIONAPPROACH

Appearance-based approaches attempt to identify facesusing global representations, i.e., illustrations based on theentire image instead of local features of the face.Appearance-based methods also known as holistic-based

methods, that mean we use complete information of a wholeface patch and perform some transformation on this patch toderive a compact representation for recognition. To be moreclearly differentiated from feature-based techniques, we cansay that feature-based methods directly derive informationfrom some detected fiducial points like eyes, noses, and lips,etc. These fiducial points are usually determined fromdomain knowledge and discard other information; whileappearance-based methods perform transformations on thewhole patch and reach the feature vector and thesetransformation basis are usually obtained from statistics.During the past thirty years, holistic-based methods attractthe most attention against other approaches, so we will focusmore on this approach. In the following sub-sections, wewill talk about the famous eigenface [2] (performed by thePCA), fisher face (derived by the LDA), and some othertransformation basis such as the independent componentanalysis (ICA), nonlinear dimension reduction technique,and the over-complete database (based on compressivesensing). More techniques could be found in [3] & [4].

A. PCAPCA is a transform that chooses a new coordinate system forthe data set such that the greatest variance by any projectionof the data set comes to reside on the first axis, the secondgreatest variance lie on the second axis, and so on. The goalof PCA is to reduce the dimensionality of the data whileretaining as much as information present in the originaldataset. Principal Component Analysis (PCA), also called asKarhunen- Loeve expansion. It is a classical featureextraction and data representation technique which is widelyused in the field of pattern recognition and computer vision[5]. In 1987, Sirovich and Kirby first used PCA toefficiently represent pictures of human faces. They arguedthat any face image could be reconstructed approximately asa weighted sum of a small collection of images that define afacial basis which is known as eigenimages, and a meanimage of the face. Within this context, Turk and Pentland[2], proposed the popular Eigenfaces method for facerecognition in 1991. Since then, PCA has been widelyinvestigated and has become one of the most successful

approaches in face recognition. In detail, goal of PCAmethod is to reduce the number of dimensions of featurespace, but still to retain principle features to minimize theloss of information.

B. LDAThe Fishers linear discriminant analysis (LDA) has beensuccessfully applied to face recognition area in the past fewyears. Fisher faces method derives from Fishers lineardiscriminant analysis (FLD or LDA); it works on the same

principle as the eigenfaces method. LDA tries to find a set of projecting vectors w best discriminating different classes.According to the Fisher criteria, it can be achieved bymaximizing the ratio of determinant of the between-classscatter matrix Sb and the determinant of the within-classscatter matrix Sw. The within class scatter Sw representshow face images are distributed closely within classes and

between class scatter matrix Sb how classes are separatedfrom each other. The goal of LDA is to maximize Sb whileminimizing Sw; the images in the training set are dividedinto the corresponding classes. LDA finds a set of vectorssuch that the fisher discriminant criterion is maximized.

Figure 2: (a) Points in two-dimensional space (b) poor separation (c)

good separationFisher Linear Discriminant (FLD) analysis, also called

Linear Discriminant Analysis (LDA) finds the line that bestseparates the points. For example, consider two sets of

points, green and blue in color, in two-dimensional space being projected onto a single line. Relying on the direction ofthe line, the points can either be combined together (Figure2a) or be separated (Figure 2b). In terminology of facerecognition this means combining images of the similar classand separate images of different classes. Images are projectedfrom an N-dimensional space to an M-1 dimensional space,where N represents the number of pixels in the image and Mrepresents the number of classes of images [6].

The LDA method, that creates an optimal projection ofthe dataset, maximizes the ratio of the determinant of the

between-class scatter matrix of the projected samples to thedeterminant of the within-class scatter matrix of the projectedsamples. The within-class scatter matrix, also known as intra-

personal matrix, illustrates variations in the appearance of thesame individual due to different lighting illuminations andfacial expression, while the between-class scatter matrix, alsoknown as the extra-personal, depicts the changes inappearance due to a difference in the identity. In this wayfisherfaces can project away some variation in lighting andfacial expression while maintaining discriminability [6].LDA is a 1D-data-based feature extraction technique; so, 2Dimage matrices must be converted into 1D image vectors

before the application of LDA. Since the resulting imagevectors are high dimensional, LDA usually encounters thesmall sample size (SSS) problem in which the within-classscatter matrix becomes singular and thus the traditional LDAalgorithm fails to use. To address this problem, a number ofextended LDA algorithms have been suggested. Amongthem, the most popular one is to use PCA for dimensionreduction prior to performing LDA, which is used in this

proposed work. Even if we were to compare PCA and LDA,

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it is obvious that LDA should have a great advantage as itwas trained with roughly two times bigger training set. The

problem was identified and researched in detail in Martinezand Kak, 2001 where it is concluded that when the trainingset is small, PCA can outperform LDA. Another importantthing to mention is that LDA is much more sensitive todifferent training sets than PCA or ICA. In order to solve this

problem, a weighted between-class scatter matrix is generallyconstructed for the Fisher criterion. In such criterion theclasses, which are closer together in the input space, are morelikely to result in misclassification and should therefore bemore heavily weighted in the input space. Thedimensionality is reduced in small fractional steps whichmake the relevant distances, which are more correctlyweighted. Similar to classical LDA, the F-LDA is notcapable to solve the small sample size problem (SSSP). TheSSS problem generally exists in high-dimensional patternrecognition tasks like face recognition, in which the numberof training samples is smaller than the dimensionality of theinput samples, due to two main factors: (1) In the high-

dimensional space the Eigen decomposition of the between-class scatter matrix is very difficult; (2) The singular scattermatrices are caused by the SSSP. In order to solve the SSSP,a hybrid technique is needed which combines the advantageof both the techniques. This thesis presents a hybrid approachfor feature extraction which combines PCA and LDA. Thisapproach has been verified to be effective by experience. Inthis research work, PCA is first utilized for dimensionalityreduction and then the application of LDA is done for thefeature extraction. We are trying to introduce a novelapproach in our proposed work and this approach will be acomplete PCA plus LDA algorithm essentially in additionwith optimal eigenvector selection using genetic algorithm,and the performance of this algorithm is superior to that ofthe previous LDA in face recognition. We demonstrate that

by introducing dimensionality reduction by PCA and featureextraction through LDA, the features that do not encodeimportant facial information are rejected, and hence the errorrate can be reduced significantly.C. ICAIndependent component analysis (ICA) performs on a set offace images by an unsupervised learning algorithm derivedfrom the principle of optimal information transfer throughsigmoidal neurons. The approach maximizes the mutualinformation between the inputs and the outputs that producestatistically independent outputs under definite conditions.

The PCA uses the second-order statistical property of thetraining set that is covariance matrix and gives projection bases which makes the projected samples uncorrelated witheach other. The second-order property only depends on the

pair wise relationships between pixels, while some importantinformation for face recognition may be contained in thehigher-order relationships among pixels. The independentcomponent analysis (ICA) [7][8] is a generalization of thePCA, which is sensitive to the higher-order statistics. Figure3 shows the representation of faces generated by various

appearance based approaches. In the works proposed byBartlett et al. [9], they derived the ICA bases from the

principle of optimal information transfer through sigmoidalneurons. In addition, they proposed to architectures fordimension-reduction decomposition, one treats the image asrandom variables and the pixels as outcomes, and the otherone treats the pixels as random variables and the image asoutcomes. ICA has the following potential advantages overPCA: 1) It provides a superior probabilistic model of the datathat better recognizes where the data concentrate in n-dimensional space. 2) It uniquely identifies the mixingmatrix W . 3) It discovers a not-necessarily orthogonal basiswhich may rebuild the data superior than PCA in the

presence of noise. 4) It is sensitive not only to the covariancematrix but also to high-order statistics in the data.

Figure 3: Representation of PCA faces, ICA-1 faces, ICA-2 faces, andLDA faces

Advantages and disadvantages of Appearance-basedapproach- The main advantage of the appearance-basedapproaches is that they do not demolish any of theinformation contained in the images by focusing on onlyrestricted regions or points of interest [10]. However, as we

discussed earlier, this same property is their greatestdrawback too since most of these approaches start out withthe basic assumption that all the pixels in the image arehaving equal importance [11]. Consequently, thesetechniques are not only computationally expensive butrequire a high degree of correlation between the test andtraining images, and do not perform effectively under largevariations in pose, scale and illumination, etc. [12].

Nevertheless, as mentioned in the above review, several ofthese algorithms have been modified and/or enhanced tocompensate for such variations, and dimensionality reductiontechniques have been exploited. Note that even though suchtechniques increase generalization capabilities, thedisadvantage is that they may potentially cause the loss of discriminative information [13], as a result of which theseapproaches appear to yield better classification results thanthe feature-based ones in general. In the latest comprehensiveFERET evaluation [14, 15], the probabilistic eigenface [16],the Fisherface [17] and the EBGM [18] methods were rankedas the best three techniques for face recognition (Eventhough the EBGM method is feature-based in general, itssuccess depends on its application of holistic neural networkmethods at the feature level).

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IV. F EATURE -BASED APPROACH

We have briefly compared the differences between holistic- based methods and feature-based methods based on what theinformation they use from a given face patch, and in another

point of view, we can say that appearance-based methodsrely more on statistical learning and analysis, while feature-

based methods exploit more ideas from image processing,computer vision, and domain knowledge form human. Inthis section, we discus two outstanding features for facerecognition, the Gabor wavelet feature and the local binary

pattern. A. Gabor FeaturesThe application of Gabor wavelet for face recognition is

pioneered by Lades et al.s work [19]. In their researchwork, the elastic graph matching framework is used to findfeature points, build the face model and to perform distancemeasurement, while the Gabor wavelets are used forextracting local features at these feature points, and a set ofcomplex Gabor wavelet coefficients for each point is called

a jet. Graph matching based methods normally requires twosteps to construct the graph g I for a facial image I andcalculates its similarity with a model graph g M. In the firststep, g M is shifted within the input image to derive theoptimal global offset of g I while retaining its shape rigid.Then in the second step, each vertex in g I is shifted in atopological constraint to remunerate the local distortions thatcaused by rotations in depth or expression variations. It isactually the distortion of the vertices which makes the graphmatching procedure elastic. To achieve these two steps, acost measure function S(g I, gM) is neccesarily to be definedand these two steps abort when this cost measure functionreaches the minimum value. Lades et al.s [19] used asimple rectangular graph to model faces in the databasewhile each vertex is without the direct object meaning onfaces. In the database building stage, the deformation

process mentioned above is not included, and therectangular graph is manually placed on each face and thefeatures are extracted at individual vertices. When a newface I comes in, the distance between it and all the faces inthe database are required to calculate, that means if there aretotally N face samples are present in the database, we haveto construct N graphs for I based on each face sample. Thismatching process is very computationally expensiveespecially for large database. Figure 4 shows an example ofa model graph while figure 5 depicts object-adaptive gridsfor difference poses. As we know that, face recognition is

not a difficult task for human beings, selection of biologically motivated Gabor filters is well suited to the facerecognition problems. Gabor filters are used to model theresponses of simple cells in the primary visual cortex andthey are simply plane waves limited by a Gaussian envelopefunction. An image can be characterized by the Gaborwavelet transform that allow the description of both thespatial frequency structure and spatial relations. One of thetechniques used in the literature for Gabor based face

recognition is mainly based on employing the response of agrid that represent the facial topography for encoding theface. High-energized points can be used in comparisonswhich form the basis of this work, instead of using the graphnodes. This approach not only minimizes computationalcomplexity, but also improves the classification

performance in the presence of occlusions.

Figure 4: The graphic models of face images. The model graph(a) a face stored in the database (b) a deformed graph

Figure 5: The object-adaptive grids for difference poses.Unlike the eigenfaces method, elastic graph matchingmethod is more robust to variations in illumination, sinceinstead of directly using pixel gray values, Gabor wavelettransforms of images is being used. However, detection

performance of elastic graph matching method is reportedsuperior than the eigenfaces method, the elastic graphmatching approach is less attractive for commercial systems,due to its computational complexity and execution time.Even though use of 2D Gabor wavelet transform seems to besuitable to the problem, graph matching makes algorithm

bulky. Furthermore, as the local information is derived fromthe nodes of a predefined graph, some details on a face, thatare special features of that face and could be very useful inrecognition task, might be lost. This method is also robust tovariations in illumination as a property of Gabor wavelet,that is the major problem with the eigenface approaches.

B. Binary FeaturesWith appearance-based methods, image filters, such asGabor wavelets, are applied to either the whole-face orspecific face-regions to extract the appearance changes ofthe face. Due to their superior performance, the major workson appearance-based methods have focused on using Gabor-wavelet representations [22,23,24,25]. However, it is bothtime and memory intensive to convolve face images with a

bank of Gabor filters to extract multi-scale and multi-orientational coefficients. In this work, we empirically studyfacial representation based on Local Binary Pattern (LBP)features [25,26] for person-independent facial expressionrecognition. LBP features were proposed originally fortexture analysis, and recently have been introduced torepresent faces in facial images analysis. The mostimportant properties of LBP features are their toleranceagainst illumination changes and their computational

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simplicity. We examine different machine learning methods,including template matching, Support Vector Machine(SVM), Linear Discriminant Analysis (LDA) and the linear

programming technique, to perform facial expressionrecognition using LBP features. Our study demonstratesthat, compared to Gabor wavelets, LBP features can bederived very fast in a single scan through the raw image andlie in low-dimensional feature space, while still retainingdiscriminative facial information in a compactrepresentation. The original LBP operator was introduced byOjala et al. [25], and was proved a powerful means oftexture description. The operator labels the pixels of animage by thresholding a 3x3 neighborhood of each pixelwith the center value and considering the results as a binarynumber, and the 256-bin histogram of the LBP labelscomputed over a region is used as a texture descriptor. Thederived binary numbers (called Local Binary Patterns orLBP codes) codify local primitives including different typesof curved edges, spots, flat areas, etc (as shown in Fig. 6), soeach LBP code can be regarded as a micro-texton. The

limitation of the basic LBP operator is its small 3x3neighborhood which cannot capture dominant features withlarge scale.

Figure 6: Basic LBP operatorAdvantages and disadvantages of feature-basedapproach-The main advantage offered by the featured-basedtechniques is that since the extraction of the feature points

precedes the analysis done for matching the image to that ofa known individual, such methods are relatively robust to

position variations in the input image [10]. In principle,feature-based schemes can be made invariant to size,orientation and/or lighting [20]. Other benefits of theseschemes include the compactness of representation of theface images and high speed matching [21]. However, thegeometric feature-based methods usually require accurateand reliable facial feature detection and tracking, which isdifficult to accommodate in many situations. The majordisadvantage of these approaches is the difficulty ofautomatic feature detection (as discussed above) and the factthat the implementer of any of these techniques has to makearbitrary decisions about which features are important. Afterall, if the feature set lacks discrimination ability, no amount

of subsequent processing can compensate for that intrinsicdeficiency [20].

V. T EMPLATE -BASED METHODS

The recognition system based on the two methodsintroduced above usually perform feature extraction for allface images stored in the database and train classifiers ordefine some metric to compute the similarity of a test face

patch with each class person class. To overcome variations

of faces, these methods increase their database toaccommodate much more samples and expect the trainedtransformation basis or defined distance metric couldattenuate the intra-class variation while maintaining theinter-class variation. Traditional template-matching is prettymuch like using distance metric for face recognition, whichmeans selecting a set of symbolic templates for each class(person), the similarity measurement is computed between atest image and each class, and the class with the highestsimilarity score is the selected as the correct match.Recently, deformable template techniques are proposed [28].In contrast to implicitly modeling intra-class variations (ex.increasing database), de-formable template methodsexplicitly models possible variations of human faces fromtraining data and are expected to deal with much severevariations. In this section, we introduce the face recognitiontechnique based on the ASM and the AAM. During the facedetection process, the AAM will generate a parameter vectorc which could synthesize a face appearance that is best fittedto the face shown in the image. Then if we have a well-

chosen database which contains several significant views, pose, expressions of each person, we can achieve a set ofAAM parameter vectors to represent each identity. Tocompare the input face with the database, Edwards et al.[29] proposed to use the Mahalonobis distance measure foreach class and generate a class-dependent metric toencounter the intraclass variation. To better exploit the inter-class variation against the intra-class variation, they alsoused the linear discriminant analysis (LDA) for dimensionreduction and classification task.

VI. P ART -BASED APPROACH

There have been several researches exploiting informationfrom facial characteristic parts or parts that are robustagainst pose or illumination variation for face recognition.To be distinguished from the feature-based category, the

part-based methods detect significant parts from the faceimage and combine the part appearances with machinelearning tools for recognition, while the feature-basedmethods extract features from facial feature points or thewhole face and compare these features to achieve therecognition purpose. In this subsection, we introduced twotechniques, one is an extension system of the method andone is based on the SIFT (scale-invariant feature transform)features extracted from the face image.

A. Component-based face recognitionHeisele et al. [30] compared the performance of the

component-based face recognition against the globalapproaches. In their work, they generated three differentface recognition structures based on the SVM classifier: acomponent-based algorithm based on the output of thecomponent-based face detection algorithm, a globalalgorithm directly fed by the detected face appearance, andfinally a global approach which takes the view variation intoaccount. Given the detected face patches, the two globalapproaches have the only difference that whether the view

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variation of the detected face is considered. The algorithmwithout this consideration directly builds a SVM classifierfor a person based on all possible views, while the one withthis consideration first divides the training images of a

person into several view-specific clusters, and then trainsone SVM cluster for each of them. The SVM classifier isoriginally developed for binary classification case, and toextend for multi-class tasks, the one-versus-all and the pair-wise approaches are described. The component-based SVMclassifier is cascaded behind the component-based facedetection algorithm. After a face is detected in the image,they choose 10 of the 14 detected parts, normalized them insize and combined their gray values into a single featurevector. Then a one-versus-all multi-class structure with alinear SVM for each person is trained for face recognition

purpose. In the experimental results, the component systemoutperforms the global systems for recognition rate largerthan 60% because the information fed into the classifierscapture more specific facial features. In addition, theclustering leads to a significant improvement of the global

method. This is because clustering generates view-specificclusters that have smaller intra-class variations than thewhole set of images of a person. Based on these results, theyclaimed that a combination of weak classifiers trained on a

properly chosen subsets of the data can outperform a single,more powerful classifier trained on the whole data.

VII. C ONCLUSION

Face recognition is a challenging problem in the field ofimage analysis and computer vision that has received a greatdeal of attention over the last few years because of its manyapplications in various domains. Research has beenconducted vigorously in this area for the past three decadesor so, and though huge progress has been made. Featureextraction is one of the most popular and fundamental

problems in face recognition tasks. This paper contained adetailed survey on existing feature extraction techniques forface recognition. With a number of different databasesavailable, it is always very difficult to compare differentface recognition algorithms. Even when the same database isused, researchers may use different protocols for testing.After reviewing a number of research papers, we found twomain subfields of face recognition approaches which requireimprovements to achieve high accuracy and speed and thatare dimensionality reduction and feature extractiontechniques and feature subset selection. In summary, a facerecognition system should not only be able to cope with

variations in illumination, expression and pose, but alsorecognize a face in real-time. To speed up the process offace recognition systems, genetic algorithm can beincorporated with the existing approaches.

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