Fingerprint Image Classification and Retrieval using

Statistical Methods

Ph.D. Synopsis

Submitted To

Gujarat Technological University

For the Degree

of

Doctor of Philosophy

in

Computer IT Engineering

By

Sudhir P Vegad

Enrollment No: 119997107012

Supervisor:

Dr. Tanmay Pawar

Professor & Head

Electronics Department

BVM Engineering College

Vallabh Vidyanagar

Foreign Co-Supervisor:

Dr. Arun Somani

Associate Dean for Research

IOWA State University

Ames, IA

Table of Contents

Page

1. Title of the Thesis and Abstract............................................................................. 1

2. Brief description on the state of the art of the research topic................................ 2

3. Objective and Scope of the work........................................................................... 8

4. Original contribution by the thesis......................................................................... 9

5. Methodology of Research, Results / Comparisons................................................ 10

6. Achievements with respect to objectives............................................................... 13

7. Conclusions............................................................................................................ 13

8. List of publication arising from the thesis............................................................. 15

9. References.............................................................................................................. 16

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1. Title of the Thesis and Abstract

1.1 Title of the Thesis

Fingerprint Image Classification and Retrieval using Statistical Methods.

1.2 Abstract

Human fingerprints are popular biometrics due to its characteristics like universality,

uniqueness, permanence, collectability and acceptability. Fingerprints are rich in details

which are known as minutiae, which can be used as identification marks to uniquely identify

the fingerprint for various purposes. There are four basic patterns of fingerprint ridges; arch,

left loop, right loop and whorl.

Fingerprint classification is an important indexing scheme to narrow down the search of

fingerprint database for efficient large-scale retrieval. It is still a challenging problem due to

the intrinsic class ambiguity and the difficulty for poor quality fingerprints. Fingerprint

retrieval is a pre-requisite step for many applications like recognition and registration.

Feature extraction plays a very important role in retrieval process. The work presented in this

research has two phases; classification and retrieval. In classification phase, scale and rotation

invariant features are extracted to enhance the local information from the fingerprint images

for powerful representation. These features strongly represent the similarities of fingerprint

images of the similar class. Classification is carried out by applying these features in

classification models. The retrieval phase match the fingerprint image features using distance

matching methods to retrieve similar images from the class declared from classification

phase. The experimental results and comparisons on FVC2000, FVC2002, FVC 2004 and

NIST-4 databases have shown the effectiveness and superiority of the proposed method for

fingerprint classification and retrieval.

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2. Brief description on the state of the art of the research topic

The identification of a person with an AFIS (automated fingerprint identification system)

requires a comparison of his/her fingerprint with all the fingerprints in a database [1]. This

database may be very large in many forensic and civilian applications, which leads to long

processing time and deteriorated accuracy; hence it is unsuitable in real time applications. A

common strategy to speed up the search is to divide the fingerprint database in to a number of

groups that have similar properties. Fingerprint classification that assigns a fingerprint to a

class (based on predefined classes) is an effective way.

Fingerprint classification has generated great interest due to its importance and intrinsic

difficulty and there are number of approaches proposed in the literature. A typical fingerprint

classification algorithm usually extracts a representative feature set to capture the

individuality of each fingerprint and then does some strategies to determine the fingerprint

class.

2.1 Fingerprint Classification

The first phase of is of fingerprint image classification. Most of the classification algorithms

are classifying fingerprint image in five classes; Arch, Left Loop, Right Loop and Whorl. Kai

Cao, Liaojun Pang, Jimin Liang amd Jie Tian [1] presented a hierarchical classifier which

estimates the orientation of fingerprint using root filtering. Diffusion model is used to

regularize the orientation to make classification rotation invariant. The classification is

performed in five stages based on the singular point detection. In the first stage, arch is

distinguished using complex filter responses. The second stage distinguishes whorl by using

core point and ridge line flow classifier. The third stage identifies arch and whorl by k-NN

classifier by using orientation fields and complex responses. Ridge line flow classifies loop

arch. SVM is used for final classification.

Nannevan Noord and EricPostma [4] proposed the use of conventional neural networks to

make image classification scale-invariant. The combination of scale-variant and scale-

invariant features are used to attempt the image classification using task-relevant

characteristics at multiple scales. Significant improvement in image recognition is stated due

to the classification phase prior to recognition.

A fingerprint classification algorithm which uses Adaboost learning method is proposed by

Manhua Liu [5]. Singularities are detected using complex filters responses at multiple scales

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and feature vector is constructed at each scale with relative position and direction of

singularities. Adaboost learning method is applied on the decision tree to generate classifier

for fingerprint classification. The experiments are carried out on NIST-4 database.

Shing Chyi Chua, Eng Kiong Wong and Alan Wee Chiat Tan proposed an algorithm for

fingerprint classification by detecting the singular point via quantization [18]. A singular

point is detected using a quantization approach on the orientation field of fingerprint image

and core-delta points are located by the changes of the gray level around a 2 x 2 window. The

unwanted singular points are removed with the application of edge-trace-cum-core-delta-

pairing algorithm and merging-and-pruning algorithm. The experiments have been carried

out on NIST-4 database for 5-class and 4-class classification.

Crease features are used for classification by observing crease features as strips in the

fingerprint images [22]. Masking is used to remove noise from the extracted crease features.

Minimum bounding box and convex hull is used to determine the size of every crease as it

this feature is found robust to distortion and classification. The experiments are performed on

Hong Kong PolyU High Resolution Fingerprint.

An approach is discussed by S. Govindaraju and G. P. Ramesh Kumar for retrieving medical

images using robust features [3]. The method applies the SURF algorithm in the detection,

description, extracting reference images and matching feature points in the image

respectively. In the process of feature point matching, the false matching points are

eliminated. Clusters are formed with the descriptors by applying BoF, centroids are formed

and histogram is generated as an pre-requisite step for retrieval.

Local Binary Pattern (LBP) features are used by Guoying Zhao, Timo Ahonen, Jiri Mata, and

Matti Pietikainen [14] for rotation invariant description of image and video description. The

LBP operator is extended to use the circular neighborhood of different sizes. To make

rotation invariant representation, bit pattern is circularly rotated to the minimum value. The

experiments are carried out on Outex_TC_00012 database.

The magnitude component of LBP is utilized in leaf image classification by Anilkumar

Muthevi [19] in which evaluate a value before generating bit pattern, which find the

relativeness within a 3x3 neighborhood by using a value called threshold that is multiplied

with the corresponding weights. The experiments are carried out on the various leaves

databases to determine texture features using LBP approach.

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Image classification using Gray Level Co-occurrence Matrix (GLCM) is proposed by A

Suresh and K L Shanmuganathan [10]. GLCM is calculated from original texture image and

the differences are calculated along the first singleton dimension of input texture image.

Contrast, homogeneity, energy and correlation are calculated from GLCM. k-NN classifier is

used for classification. System performance is evaluated by using Brodatz database and

compared with the methods PSWT, TSWT, the Gabor transform and Linear Regression

Model.

Another approach of texture extraction from image is proposed by P. Mohanaiah, P.

Sathyanarayana and L. GuruKumar [9]. Features namely, Angular Second Moment,

Correlation, Inverse Difference Moment and Entropy are computed. The experimental results

are shown stating the texture features having high discrimination accuracy and requires less

computation time. Hence GLCM is claimed having reduced image compression time in the

process of converting RGB image to gray level image.

Ray-I Chang, Shu-Yu Lin, Jan-Ming Ho, Chi-Wen Fann and Yu-Chun Wang presented k-

means and k-NN clustering algorithms for content based image retrieval system. Training of

the system is performed by applying segmentation and grid generation, followed by k-means

clustering and neighborhood table generation. The query image is going the same stages

except k-NN clustering rather than k-means clustering. Future enhancements are also

proposed for improved grid module to minimize the loss of information due to quantization.

Tran Son Hai, Computer Science Department and Nguyen Thanh Thuy have proposed the

classifier k-NN for the classification of facial expressions [36]. Independent Component

Analysis (ICA) is used to extract facial features. k-NN classifier uses the ratio features for

classification along with Artificial Neural Networks. Basic seven universal facial expressions

are the classes used for classification.

2.2 Fingerprint Retrieval

Fingerprint Retrieval is process of finding similarities among the query fingerprint image and

that of from the fingerprint database. Using strong feature extraction methods which can

represent the fingerprint images uniquely are used before matching. Distance matching

strategies are used to find the similarities among the fingerprint images and n closest images

are presented as a result [15][28].

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K. C. Leung and C. H. Leung [11] have proposed the method for improving fingerprint

retrieval by artificially expanding the set of training samples of fingerprints using spatial

modelling technique. Bays classification approach is used to classification and recognition of

fingerprint images. The experiments have been carried out on FVC and NIST-4 databases.

Yashika Birdi and Er. Jagroop Kaur proposed a method based on wavelets in which first of

all an input fingerprint image is decomposed upto three levels using DWT and then textural

features are extracted [15]. Mean, standard deviation and energy computed to build feature

vector. Similarity of fingerprint images is derived using Chi-square approach. Evaluation of

the system is done through various parameters like Specification, Sensitivity, Positive

Predictive Value, Negative Predictive Value, Recognition Rate and Accuracy.

Another wavelet based retrieval is discussed by Javier A. Montoya Zegarra, Neucimar J.

Leite and Ricardo da Silva Torres [16]. Similar to the above approach, the fingerprint image

is decomposed in different levels in wavelet domain and feature vector is built using mean

and standard deviation. Different wavelets used in their study include: Gabor wavelets, tree-

structured wavelet decomposition using both orthogonal and bi-orthogonal filter banks, as

well as the steerable wavelets. Square Chord Similarity measure is used for retrieval.

Among the features used for accurate retrieval, SURF is popular due to its fast feature

extraction approach. Sukhmanjeet Kaur and Mr. Prince Verma proposed and integration of

Artificial Neural Network (ANN) using SURF and SVM [28]. The approach applies SURF

for the detection description of the features. During the feature matching phase, false

matching points are eliminated. The features built with SURF is applied to SVM and ANN

for further classification.

SURF has been presented as a novel scale and rotational invariant detector and descriptor by

Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Goola [33]. For image convolution

stages, the image is converted in Integral Image which makes the computations for the

summation of any rectangular box faster than any other feature extraction approaches. SURF

uses Hessian matrix-based measures for the detectors and detectors. SURF is applied for the

camera calibration for image registration and object recognition.

Nursabillilah Mohd Ali, Soon Wei Jun, Mohd Safirin Karis, Mariam Md Ghazaly and Mohd

Shahrieel Mohd Aras have presented an approach of classification of the object by extracting

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SURF features and statistical classifier [37]. The recognition of the object is performed by

extracting the SIFT features.

E.N. Mortensen, Hongli Deng and L. Shapiro proposed SIFT as a descriptor which can find

matches between features with unique neighborhood along with the global context vector for

detecting the matching points of multiple images of a scene [35]. The SIFT helps in the

detailing of local descriptors which is enhance by augmenting with global context vector to

minimize the mismatches when multiple local descriptors are similar. Experiments are

performed on veriety of scene images and compared matching accuracy between the SIFT

with global context to that without.

Hiroharu Kato and Tatsuya Harada have presented the Bag of Features (BoF) to reconstruct

images. BoF is used as a histogram histogram of quantized descriptors extracted densely on a

regular grid at a single scale [34]. Large-scale image database is used to estimate the spatial

arrangement of local descriptors. They proposed a heuristic method to optimize jigsaw puzzle

problem with adjacency and global location costs. BoF are extracted from various image

categories and demonstrated the reconstruction of original images.

Chih-Fong Tsai presented the use of BoF for image annotation. The proposed approach is to

help users to search the images through keywords but eliminates the labeling of images

manually [32]. As image annotation problem can be regarded as an image classification

problem, BoF classifier is used to represent global features of images. The detection of region

of interest is performed and local descriptors are computed. The descriptors are quantized to

words to generate vocabulary. BoW features are constructed by finding the occurrence of

each specific word in the image.

3. Objective and Scope of work

A fingerprint classification system must be able to analyze and recognize the class, i.e. Arch,

Left loop, Right loop and Whorl of a fingerprint image. The system should also be able to

recognize fingerprint images accurately without human intervention. One of the objectives of

this work is to develop a method which recognizes the fingerprint class using the statistical

features extracted from the fingerprint images. Another objective is to develop a method of

uniquely representing the fingerprint image so that most relevant images are retrieved in the

retrieval phase which contains the similar fingerprint image.

The major objectives of this research work are summarized as below:

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To develop an algorithm for fingerprint features extraction to recognize fingerprint

class. Scale and rotation invariant features are extracted and given to the classifiers for

class identification. The algorithm is to be tested on NIST – 4 standard database

which contains 8-bit gray scale fingerprint image pairs.

To develop an algorithm which extract robust features such that it speeds up the

process of feature extraction for retrieval. The algorithm should represent the local

interest points which makes reduction in the size of feature vector and still strongly

represent the class of fingerprints uniquely. The algorithm is to be tested on NIST – 4

database.

To develop an algorithm which uses hybrid feature extraction technique to extract

features such that the given fingerprint image can be matched with similar image class

images with improved accuracy.

The scope of the work:

The work is tested on two standard databases NIST – 4 and FVC. NIST – 4 contains

image pairs while FVC databases contains eight images of fingerprints.

The work is taking in account the acquisition methods of fingerprint images. The

training and testing of the images is performed with the same databases.

4. Original contribution by the thesis

In this thesis, method of fingerprint image classification and retrieval system is suggested

with variations and combinations of feature extraction, classification and retrieval techniques.

Images from two different databases are used. One is NIST special database 4 (NIST-4),

Fingerprint Verification Competition databases (FVC2000, FVC2002 and FVC2004) and the

fingerprint images captured using optical sensor with the help of volunteers.

The main contributions of this thesis are summarized as follows:

The performance of the classification method strongly depends on the accuracy of the

features extracted. A hybrid feature extraction method is developed that uses the

combinations of different feature extraction algorithms. The feature extraction

algorithms using LBP and GLCM are used collectively to extract features from

fingerprint images. The concatenated features are given to the Support Vector

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Machine classifier for classification. K-nearest neighbor (k-NN) classifier is also used

for the comparison of the classification results.

BoF classifier which is popular for document classification is experimented with the

fingerprint images to check the possible improvements in the accuracy level of

classification. The Speed-Up Robust Feature (SURF) algorithm is used to extract the

features to test BoF classifier along with the hybrid feature extraction method. Both

the classification algorithms are evaluated on NIST-4 and captured fingerprint images

databases.

It is essential to extract the features which are scale and rotation invariant to retrieve

fingerprint images with accuracy. In retrieval phase, Scale Invariant Feature

Transform (SIFT) features and SURF are used collectively to extract features from the

fingerprint images of the same class. The concatenated features are extracted from

these algorithms are given to BoF and other distance matching algorithms for

indexing. The retrieval algorithms are evaluated on NIST-4, FVC and captured

fingerprint image databases.

5. Methodology of research, results and comparisons

The work carried out in this research is focusing on basic three aspects of retrieval process;

feature extraction, classification and retrieval. The other aspect of the system is the kind of

input images. Standard image database NIST-4 contains the gray scale fingerprint image

pairs taken by a specific acquisition source. FVC2000, FVC2002 and FVC2004 databases

contains eight images of a fingerprint acquired from a specific source. The captured image

using optical sensor with the help of volunteers contains four images for a fingerprint.

5.1 Fingerprint Classification Algorithm - 1

The work proposed for fingerprint classification in this algorithm has major two steps; feature

extraction using LBP and GLCM and classification using Support Vector Machine and k-

Nearest Neighbor classifiers.

5.1.1 Hybrid Feature Extraction using LBP and GLCM

5.1.1.1 Local Binary Pattern (LBP)

Feature extraction is the most important phase for classification and retrieval. The proposed

work use of LBP features due to their strong local feature representation and computational

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simplicity. LBP features describe changes of ridge patterns of fingerprint images of different

classes and hence, would be efficient and effective for classification. The LBP operator labels

the pixels of an image by thresholding a 3×3 neighborhood of each pixel with the centre

value and considering results as a binary number, called as LBP Codes. After labelling a

image with LBP operator, a histogram of the labelled image is found. This 256-bin histogram

of LBP labels computed over a region contains information about the distribution of the local

micro-patterns, such as edges of the ridges, ridge end-points and ridge bifurcations, so can be

used as a texture descriptor to describe fingerprint image characteristics.

A LBP histogram computed over the whole face image encodes only the occurrences of the

micro-patterns without any indications about their locations. The extracted feature histogram

represents the local texture and global shape of fingerprint images.

5.1.1.2 Gray Level Co-occurrence Matrix (GLCM)

GLCM is a statistical method of examining texture that considers the spatial relationship of

pixels. The GLCM functions create GLC Matrix by characterizing the texture of an image by

calculating how often pairs of pixel with specific values and in a specified spatial relationship

occur in an image and then extract statistical measures from this matrix. Statistics can derived

from this matrix provide information about the texture of an image. Few of them are;

Entropy, Correlation, Energy and Homogeneity.

Entropy is a statistical measure of randomness that can be used to characterize the texture of

the input image. Correlation measures the dependency of grey levels of specified neighboring

pixel pairs. Energy provides the sum of squared elements in the GLCM. It gives high value

when image has very good homogeneity, i.e. when pixels are very similar. Homogeneity

measures the closeness of the distribution of elements in the GLCM to the GLCM diagonal.

The features from LBP and GLCM concatenated to obtain a single vector that is given to the

classifier.

5.1.2 Classification

5.1.2.1 Classification using kNN Classifier

k-Nearest Neighbor classifies data sets based on their similarity with neighbors where „k‟

stands for number of data set items that are considered for the classification. kNN makes

predictions for a new instance(x) by searching through the entire training set for k most

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similar instances (neighbors). To determine the similarity, a distance measure is used. For

real valued input variables, Euclidian distance is used.

5.1.2.2 Classification using SVM Classifier

Support Vector Machine (SVM) is one of the known algorithms for pattern and image

classification. SVM is designed to separate of the training images of different classes in

feature space. It builds the optimal separating hyper planes based on a kernel function. All

images of which feature vector lies on one side of the hyper plane are belong to one class.

5.2 Fingerprint Classification Algorithm – 2

The work proposed for fingerprint classification in this algorithm has major two steps; feature

extraction using Speed Up Robust Features (SURF) and classification using BoF.

5.2.1 Feature Extraction using SURF

Feature extraction algorithm using SURF selects the interest points from the images at

different scales using Hessian Matrix which can represent the texture of the image uniquely.

In the initial step, the input image is represented as an Integral Image which minimizes the

cost of calculating sum of intensity values of a rectangular region. With the neighborhood of

every interest points, then it builds the feature vector which is distinctive and robust to noise

and geometric deformations. To make features invariant to rotation, Haar wavelet responses

are calculated within the circular neighborhood abound the interest point.

5.2.2 Classification using BoF

A BoF method is one that represents fingerprint images as orderless collections of local

features. In the first step, from the features extracted by SURF technique, it clusters them into

the pair of image and set of features contained in that image, to build a visual vocabulary.

Then it records the count of each clusters appear in the image to create a normalized

histogram representing cluster vector, which is known as BoF. Then it uses Nearest Neighbor

method it assigns the cluster to the fingerprint image to declare the class.

5.3 Fingerprint Retrieval Algorithm

After declaring the class of fingerprint image, the retrieval of similar fingerprint images is

performed within the class declared.

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5.3.1 Hybrid Feature Extraction using SURF and SIFT

In the first step of SIFT feature extraction, algorithm starts with the scale-space extrema

detection in which it identify the potential interest points by difference of Gaussian function

(DoG). Then it determines the location and scale of interest point by detecting local maxima

and minima of DoG. Interest point orientation can be calculated by orientation of histogram

of local gradients. In the final stage , the local image gradients are measured at the selected

scale in the region around interest point and transformed into scale-invariant coordinates

relative to local features and feature vector is generated.

The features from SIFT and SURF concatenated to obtain a single vector that is given to the

retrieval algorithm.

5.3.2 Retrieval using BoF

As explained earlier, BoF generate the clusters of the hybrid features generated in the

previous step and tries to find the existence of feature cluster in the fingerprint image. Then it

ranks each image based on its distance from the given fingerprint image, calculated based on

Nearest Neighbor strategy. The rank results are ordered and the nearest n images whose

distance shows the nearest images are displayed.

6. Achievements with respect to objectives

The objective of this work was to devise a method for accurate fingerprint image

classification which minimizes the comparisons at retrieval stage. The retrieval method is

expected to be accurate and speedy.

Different methods are proposed to achieve the stated objective. The feature extraction

methods discussed in algorithm 1 are utilizing the advantages of both the methods LBP and

GLCM to improve the classification rate. The SURF method, discussed in algorithm 2,

speeds up the feature extraction process and hence improves the system response time. The

classification is performed on standard databases as well as manually captured images.

The retrieval method proposed for fingerprint retrieval was carried out by utilizing the scale

and rotation invariant features SIFT and SURF. Feature extraction is fast due to the SURF

characteristics. Retrieval results are showing significant improvement using standard as well

as manually captured image databases.

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7. Conclusion

The feature extraction methods used for classification of fingerprint images are evaluated by

generating confusion matrices by all combinations of four feature extraction methods (LBP,

GLCM, Hybrid and SURF) and three classification methods (SVM, kNN and BoF). The

experiments are performed on NIST-4 fingerprint image database. The experiment results

are cross-validated using k-fold model. The results shown significant improvement when

Hybrid and SURF methods used with BoF classifier.

Further, the fingerprint image retrieval is performed by using Hybrid feature extraction

method and BoF for retrieval. The experiments are carried out on NIST-4 fingerprint image

database and FVC databases by the combinations of three feature extraction methods (SIRF,

SIFT and Hybrid) with BoF. The performance of the system is evaluated based on the

performance measures precision and recall. Due to limited number of images of single

fingerprint images, recall gives better evaluation analysis than precision. The results of

Hybrid feature extraction method with BoF shows improvement in retrieval rate.

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8. List of publications

[1] Sudhir Vegad, Dr. Tanmay Pawar, "Fingerprint Image Retrieval: A Novel Approach”,

Springer International Conference on Intelligent Systems and Signal Processing, ISSP-

2017, March 2017

[2] Sudhir Vegad, Dr. Tanmay Pawar, “Fingerprint Image Retrieval using Statistical

Methods”, International Journal of Computer Science and Information Security, ISSN –

1947 5500, Vol. 15 (11), pp 246-249, November 2017

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