A Novel Method for Gender Classification Using DWT and SVD Techniques

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1 A Novel Method for Gender Classification Using DWT and SVD Techniques Pallavi Chand Department of Electronics and Instrumentation Engg. Institute of Technical Education and Research, S O A University, Bhubaneswar , Orissa Shubhendu Kumar Sarangi Department of Electronics and Instrumentation Engg. Institute of Technical Education and Research S O A University, Bhubaneswar , Orissa Abstract In today s time fingerprint plays a very important role, whether it is to link the suspect in a crime scene or to find an unknown person. Fingerprints are one of the most mature biometric technologies and are considered legitimate proofs of evidence in courts of law all over the world. The main objective of this paper is to find a link between gender of a person and his/her fingerprint. The gender of an unknown fingerprint was found out by classifying the frequency and spatial domain vector of the input image. The 2D-Discrete Wavelet Transformation (DWT) was used to find the frequency domain vector and Singular Value Decomposition (SVD) was implemented in order to find the spatial feature of the non-zero singular values. Both the outputs of SVD and DWT are combined to form the feature vector. The K-nearest neighbour classifier is used to classify the fingerprint. The method is experimented with the internal database of 100 fingerprints of left hand index finger, 50 males and 50 females belonging to same age group. Keywords Fingerprint, Biometric technologies, Discrete Wavelet Transformation, Singular Value Decomposition, K- nearest neighbour classifier. I. INTRODUCTION Gender of a person can be identified using different biometric traits such as face, iris, retina, speech, gait, hand geometry and fingerprint. Fingerprint is one of the most common traits of human and can be easily obtained. Now a days thumbprints and fingerprint of each finger are taken in order to provide the identity proof to that particular person, e.g. to get a passport or a unique identity card in India, one had to give the impression of his/her thumb and fingerprints. A person s fingerprint is permanent even before they are born. Around 6-8 weeks after conception the volar pads (ball like structures that make up the contour of the fetal hand) form; by weeks after conception the volar pads begin to recede; around the 13th week skin ridges appear and take the shape of the receding volar pad; lastly around the 21st week after conception the fingerprint patterns are complete. A Fingerprint is the representation of the epidermis of a finger; it consists of a pattern of interleaved ridges and valleys. Fingertip ridges evolved over the years to allow humans to grasp and grip objects. Like everything in the human body, fingerprint ridges form through a combination of genetic and environmental factors. This is the reason why even the fingerprint of identical twins is different [2]. Fingerprint is an impression of friction ridges, from the surface of the finger-tip. Fingerprints have been used for personal identification for many decades; more recently becoming automated due to advancements in the computing capabilities Fingerprints have some important characteristics that make them invaluable evidence in crime scene investigations: 1. A fingerprint is unique to a particular individual, and no two fingerprints possess exactly the same set of characteristics. 2. Fingerprints do not change over the course of person s lifetime (even after superficial injury to the fingers). 3. Fingerprint patterns can be classified, and those classifications then used to narrow the range of suspects [3]. In this work, gender identification is mainly based on DWT and SVD. DWT is used to determine the energy vectors and SVD is used for getting Eigen vectors. Figure 1 illustrates the DWT and SVD based Gender Classification system. Fig. 1 Block Diagram of DWT and SVD based Gender Classification System. Features of fingerprints vary with sexes, ethnic groups and age categories. In this case the fingerprint is obtained from the scanned image from the inked impression of the fingerprints. The paper is aimed in developing an algorithm for classifying the gender through fingerprint. 445

2 II. PREVIOUS WORKS Studies so far carried out in sex determination used the inked fingerprints and their findings are based on the spatial domain analysis of ridges [6]. Generally ridge related parameters such as fingerprint ridge count, ridge density, ridge thickness to valley thickness ration, ridge width and fingerprint patterns and pattern types were used for gender determination [6]. Earlier work on gender classification based on the ridge density shows that the ridge density is greater for female than male and analyzed fingerprints of bagathas a tribal population of Andhra Pradesh (India) and showed the evident that the males showing higher mean ridge counts than females [1]. M.D. Nithin et al [1] has applied baye's theorem on the rolled fingerprint images belonging to south Indian population and found fingerprint possessing ridge density < 13 ridges/25 mm2 is most likely to be of male origin and ridge count > 14 ridges/25mm2 are most likely to be of female. Similar results is obtained by Dr. Sudesh Gungadin MBBS as in [3] using Ridge density by counting the ridges in the upper portion of all fingers which shows that a finger print ridge of < 13 ridges/25 mm2 is more likely of male origin and finger print ridge of > 14 ridges/25mm2is more likely of female origin as in [3]. III. PREPROCESSING I have collected 100 fingerprints (50 males and 50 females) by taking inked impression on the paper and scanning it. Those images were converted to greyscale (png format) by the means of Abode Photoshop 7.0. The inked impression of the fingerprint is shown in Figure 2. Fig. 3 Enhanced image and Binarized image Enhancement techniques used on the fingerprints varies with the quality of the image and types of database used. Poor quality fingerprint image obtained is enhanced for better implementation of algorithm. IV. EXTRACRION OF FEATURE VECTORS The proposed system consists of creating an algorithm in order to classify the input fingerprint and decide the class of the fingerprint. The enhanced image was given as the input and the feature vectors are obtained. Discrete Wavelet Transformation (DWT) and Singular Value Decomposition methods are used in order to obtain the feature vectors in frequency and spatial domain respectively. The spatial domain approaches involve more computations, whereas frequency domain approaches are more flexible and involve less computation as in [6]. A. Discrete Wavelet Transformation to Obtain Feature Vector in Frequency Domain The pre-processed image undergoes DWT in order to obtain vectors in frequency domain. Discrete Wavelet Transformation is a type of transformation in which wavelets are directly sampled. The DWT of a signal x is calculated by passing it through a series of filters. Firstly the sample image was passed through a low pass filter through impulse response g resulting in convolution shown in equation (1). Fig. 2 Inked impression of fingerprint and its conversion to gray scale The input fingerprint was enhanced by a series of techniques. Firstly the image was resized to 512 X 512 no of pixels. Then the resized image undergoes different enhancement techniques like Normalization, histogram equalization, contrast and brightness enhancement. The fingerprint image obtained undergoes image enhancement for improving quality of the ridges and valleys. The enhanced gray-scale image is then converted into binary image. The output of the preprocessing stage is shown in Figure 3. The signal is also decomposed simultaneously by using a high pass filter h. The two filters used had to be related with each other. This decomposition has halved the time resolution, since only half of each filter output characterizes the signal. Each output has half the frequency band of the input, so the frequency resolution has been doubled. This decomposition is repeated to further increase the frequency resolution and the approximation coefficients decomposed with high and low pass filters and then down-sampled as shown in Figure

3 Fig. 4 Level 2 DWT Two dimensional DWT decomposes an image into subbands that are localized in frequency and orientation. The decomposition of images into different frequency ranges permits the isolation of the frequency components as in [1]. The 2-D wavelet decomposition of an image results into four decomposed sub-band images referred to as low low (LL), low high (LH), high low (HL), and high high (HH). Each of these sub-bands represents different image properties. Most of the energy of the images is in the lower frequencies. So the further decomposition of sub band is repeated in LL sub band. For k level DWT, there are (3*k) + 1 sub-bands available. The energy of all the sub-band coefficients is used as feature vectors individually which is called as sub-band energy vector (E). The energy of each sub-band is calculated by using the equation (2) as in [1-2]. Where xk(i,j)is the pixel value of the kth sub-band and R, C is width and height of the sub-band respectively. Here each fingerprint undergoes six level of decomposition and the total number of energy vector E k at the end of the decomposition is 1 X19 vectors. effectively in least squares regression and makes partial SVD useful. 4. The SVD can be computed accurately for singular or nearly singular matrices. For a matrix of rank n, only the first n singular values will be non-zero. This allows SVD to be used for solution of singular linear systems. The columns of U and V corresponding to zero singular values define the null space of A. Any real mxn matrix A can be decomposed uniquely as A = USV T (3) U=AA T (4) V=A T A (5) The singular values are ordered so that the largest singular values are at the top left and the smallest singular values are at the bottom right, i.e., s 1,1 s 2,2 s 3,3 etc. Here, S is a diagonal matrix which contains the square root Eigen values from U or V in descending order. These values are stored in a vector called Eigen vector (V). The input image in SVD is a resized image of dimension 512X512, and then the Eigen vector V will be of size 1X512. C. Combined Vector After the image undergoes DWT and SVD, the feature vectors are stored separately. DWT has 1X19 and SVD has 1X512 feature vectors, which are combined to form a sum total of 1X531 feature vectors and stored in the database. As there are 100 fingerprints in the database, the total no of feature vector stored is equal to 531X100. The graph plotted after getting feature vector is shown in Figure 5. B. Singular Value Decomposition to Obtain Feature Vector in Spatial Domain The pre-processed image undergoes SVD in order to obtain fingerprint in spatial domain. Singular value decomposition takes a rectangular matrix A, (where A is a n x p matrix). It is a form of product decomposition of a matrix in which A is decomposed into a product U, S and V T where U and V are orthonormal and S is a diagonal matrix. The values of A can be real or complex, but the real case dominates applications in machine learning. The most prominent properties of the SVD are: 1. The decomposition of any real matrix has only real values 2. The SVD is unique except for column permutations of U, S and V 3. If you take only the largest n values of S and set the rest to zero, you have a least squares approximation of A with rank n. This allows SVD to be used very Fig. 5 Plotting of Combined Feature Vector V. GENDER CLASSIFICATION 100 fingerprints (50 males and 50 females) have been used to create the database. Those fingerprints were obtained by scanning the inked impression of left index finger. The feature vector for each fingerprint is stored in the database. Each fingerprint has 1 X 531 feature vectors. Hence the database has sum total of 100 X 531 feature vectors. 447

4 In classification stage mainly deals with two sub stages namely, Preparatory stage and Decision stage. In the Decision stage in order to decide the Gender of the fingerprint a K- Nearest Neighbour (KNN) classifier is used. A. Preparatory stage In this stage DWT and SVD are implemented on every fingerprint (100 fingerprints) and the combined feature vectors of each fingerprint are stored in the database. In this stage all the known fingerprints are treated as input and the combined feature vectors of those fingerprints are the outputs, which are stored in the database. The algorithm of preparatory stage is: 1. The input fingerprint (known) undergoes 6-level DWT and 19 vectors (E k ) in frequency domain are obtained. 2. Eigen vectors of those fingerprints are found out by using SVD method. Since the image is 512X512 size. So the output is of size 1X A sum total of 1X531 combined vectors are obtained. 4. Vectors of every fingerprint are stored in the database as shown in Figure The input fingerprint (unknown) undergoes 6-level DWT and 19 vectors (E k ) in frequency domain are obtained. 2. Eigen vectors of those fingerprints are found out by using SVD method. Since the image is 512X512 size. So the output is of size 1X A sum total of 1X531 combined vectors are obtained 4. Those vectors are compared against the stored vectors in the database using KNN classifier. 5. The class (Gender) of the output is found. Fig. 7 Block Diagram of Decision Stage Hence by given an unknown fingerprint as input, the gender of that fingerprint is found out as shown in Figure 8. Fig. 6 Block Diagram of Preparatory Stage Fig. 8 Final output B. K-Nearest Neighbour Classifier The k-nearest neighbour algorithm (k-nn) is a method for classifying objects based on closest training examples in the feature space. It is one of the simplest algorithms. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of its nearest neighbour. In the classification phase, k is a userdefined constant, and an unlabeled vector (a query or test point) is classified by assigning the label which is most frequent among the k training samples nearest to that query point. C. Decision Stage In this stage the combined vectors of the unknown fingerprint is compared against that of the stored feature vectors using K-NN Classifier. Input of this stage an unknown fingerprint and the Gender of that fingerprint are found out as the output as shown in Figure 7. The algorithm of decision stage is VI. CONCLUSION The algorithm of the proposed system is written in MATLAB R2010 and run in Intel Core 2 Duo, 2.20 GHz processor with 2.00 GB memory. Here, I proposed a method for Gender Classification of fingerprints using DWT and SVD. The level 6 DWT is selected as optimum level for the gender classification. The success rate is more than 80%. The proposed system is experimented only on the optical scanned image. Better result will be obtained for digital image. Fingerprint evidence is undoubtedly the most reliable and acceptable evidence till date in the court of law. Due to the immense potential of fingerprints as an effective method of identification an attempt has been made in the present work to analyse their correlation with gender of an individual. The fingerprints of different age groups vary in size and patterns and thickness of ridges and valleys. The fingerprints of people from various ethnic groups vary. An algorithm for compressing the huge database of fingerprints has to be developed and the database of the feature vectors have to be coded to provide a simpler database structure to reduce the complexity in calculations. 448

5 VII. FUTURE WORKS In future the algorithm has to be improved with a good classification algorithm like neural networks. It has been found that improving the database is an important criterion for good classification and estimation. Ink prints, optical scanned prints and prints from artifacts all can be used in the database. In future fingerprints from different ethnic groups have to be collected for a large scale study as in [2]. In future, more work can be done in frequency domain to find different parameters and different transforms that can be applied in gender identification which will more accurate and suitable for all types of applications. This research can be further extended by enhancing the classifier using neural network and fuzzy logic tool box as in [6]. Classification algorithms can be developed that can be provided with the fingerprint feature vector for training. This will improve the strength of the algorithm as in [2]. By using complex classifier better output can be obtained. ACKNOWLEDGMENT I would like to place my gratitude to all whose cooperation was vital for the success of this paper. I would like to acknowledge and extend my heartfelt gratitude to Miss Subhalaxmi Sahoo, Asst Professor ITER, Bhubaneswar, for their invariable suggestions, guidance and constant encouragements that helped me to successfully complete this project. Even I would like to extend my gratitude to Miss Geetika Das, Miss Gayatri Mohanty and Miss Jasaswini Rath for helping me in collection of fingerprints. REFERENCES [1] P. Gnanasivam & Dr. S. Muttan Fingerprint Based Gender Classification Using Wavelet Transformation and Singular Value Decomposition IJCSI International Journal of Computer Science Issues, Volume. 9: Issue 2, No 3: pp , March 2012 [2] Rijo Jackson Tom & T.Arulkumaran Fingerprint Based Gender Classification Using 2D Discrete Wavelet Transforms and Principal Component Analysis International Journal of Engineering Trends and Technology, Volume4: Issue2: pp [3] Ritu Kaur & Susmita Ghosh Mazumdar Fingerprint Based Gender Identification using frequency domain analysis International Journal of Advances in Engineering & Technology, March 2012.Volume. 3, Issue 1, pp [4] Dr. Sudesh Gungadin, Sex Determination from Fingerprint Ridge Density, Internet Journal of Medical Update, Volume. 2, No. 2, Jul- Dec 2007 [5] P. Gnanasivam & Dr. S. Muttan Gender Identification Using Fingerprint through Frequency Domain Analysis European Journal of Scientific Research, ISSN X, Volume,.59 Issue No.2 (2011), pp , 2012 [6] Ms. Ritu Kaur1, Mrs. Susmita Ghosh Mazumdar2 & Mr. Devanand Bhonsle A Study on Various Methods of Gender Identification Based on Fingerprints International Journal of Emerging Technology and Advanced Engineering ISSN , Volume 2, Issue 4, pp , April