PALMPRINT AUTHENTICATION BASED ON GABOR WAVELET USING SLIDING WINDOW APPROACH

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1 PALMPRINT AUTHENTICATION BASED ON GABOR WAVELET USING SLIDING WINDOW APPROACH Sadiya Thazeen, Feroz Morab, Mohammed Najmus Saqhib, Seema Morab Abstract A biometric system is a pattern recognition system that recognizes a person on the basis of the physiological or behavioral characteristics that the person possesses. There is increasing interest of researchers in the development of fast and accurate personal recognition systems. In this paper, we are using Palmprint for authentication, because it is having many features. For this technical work, Line features of palmprint are used. Sliding window method is used to make the system fast by reducing the matching time. The reduction in computation time indirectly reduces the overall comparison time that makes the system fast. Further, 2-D Complex Gabor Wavelet method is used to extract features from palmprint. The extracted features are stored in a feature vector and matched by Hamming distance similarity measurement using sliding window comparison approach. Here palmprint biometric is selected for personal authentication as it is unique and relatively low resolution images (less than 100dpi) are sufficient to extract its unique features. Palmprint authentication is an emerging field of research with many challenges such as large set of images, improper texture conditions. The paper covers various steps involved in Palmprint authentication using Gabor Wavelets. Index Terms Biometry, Gabor Wavelets, Palmprint Authentication, Sliding Window. I. INTRODUCTION Biometrics is the automatic recognition of a person using distinguishing traits. A more expensive definition of biometrics is any automatically measurable, robust and distinctive physical characteristic or personal trait that can be used to identify an individual or verify the client identity of an individual [1]. Measurable means that the characteristic or trait can be easily presented to a sensor, and converted into a digital format. The robustness of a biometric is the extent to which the characteristic or trait is subjected to significant changes over time. These changes can occur as a result of age, injury, illness, occupational use or chemical exposure. A highly robust biometric does not change significantly over time while a less robust biometric will change. Distinctiveness is a measure of the variations or differences in the biometric pattern among the general population. Higher the degree of distinctiveness, the more unique is the individual or the identifier. Biometrics utilize something you are to authenticate identification. This might include fingerprints, retina pattern, iris, hand geometry, voice password, or signature dynamics. Building Blocks: Sensor (that responds to biological stimulus, such as fingerprints, voice, retinas, palm pressure dynamics to generate signal that can be measured or interpreted), Feature Extraction, Search & Match Algorithm, Identity Database[2],[3]. Some of the examples of biometrics are: Iris Scan, Retinal Scan, Facial Recognition, Voice Recognition, Fingerprint, Dynamic Signature verification, Keystroke Dynamics, Hand/Finger Geometry. II. LITERATURE SURVEY Two kinds of biometric indicators (features) can be extracted from the low-resolution hand images: 1. Hand geometry features area/size of palm, length & width of fingers 2. Palmprint features principal lines, wrinkles, ridges, delta points & datum points The problem of personal verification using palmprint features has drawn considerable attention and researchers have proposed various methods. One popular approach considers palmprints as textured images which are unique to every individual. Therefore, analysis of palmprint images using Gabor filters [10], wavelets [2],[4],[5] SIFT (Scale Invariant Feature Transformation) [7],[8] and local texture energy [6] has been proposed in the literature. Some authors like G.Shobha et al [1] have used above two features for authentication purpose and suggested that the use of hand geometry features and palmprint features gives 3075

2 higher performance accuracy. Hariprasath.S et al [5] used iris recognition with palmprint to attain more accuracy. The key features in authentication process are accuracy and speed to use in real time. So many methods have been used for comparison purpose in this regard. Xiang-qian et al [9] have used the fusion of Orientation code and Differential code for matching. Also, use of Hamming distance [10], SAX (Symbolic Aggregate approximation) [8], ICP (Iterative Closest Point) algorithm [6] has been incorporated in literature. Although Refs. [1],[3] are accurate, they require more time for matching comparable to our method. Using Sliding window concept, we made system high speed as well as accurate. Some authors used two features for extracting ROI (Region of Interest): Hanmandlu Madasu et al [2] used wavelet and fuzzy features, Aythami Morales et al [7] used OLOF (Orthogonal Line Ordinal Features) and SIFT features. Xiang-qian et al [9] utilized orientation and differential features whereas Xiangqian el at [10] used Phase and Orientation features. Also comparison is done between these two features. But this method uses only Gabor wavelet features for extraction and comparison is done between the Hamming distances of Gabor features at 7 orientations. Wei Li Bob et al [6] method and Hanmandlu Madasu et al [2] method used for only middle or small size database where as this method can be used for any database size. Some process, Refs.[1],[3],[5] require high resolution images for processing. But in this method images with low resolution (less than 100 dpi) are sufficient to extract unique features of palmprint. From [2] we realized that Gabor features are sensitive to slight changes in size and orientation of palm. Since this paper does not use hand geometry but only palmprint, also image is cropped to fixed size (i.e., 60X60) and orientations are also fixed (i.e., 0º, 30º, 60º, 90º, 120º, 150º & 180º), hence Gabor wavelet is adopted in this method. III. PROBLEM DEFINITION AND OBJECTIVE The proposed work addresses the authentication of palmprint images. An image enhancement method is used for improving the image quality by improving the contrast of texture and small structures. The whole method is divided into pre-authentication and authentication system. genuine or imposter is identified with the help of Reference threshold values stored in Pre-authentication system database. Objective: Some of the objectives that are identified during palmprint authentication are listed below: Improvement of quality of images. Preservation of the small scale structures. Matching the input palm with every palm present in training database by Hamming distance of Gabor feature extracted using Sliding window method. If the match occurs, then returning the equivalent image corresponding to input image. Otherwise, invalid image will be shown. IV. PROPOSED METHODOLOGY A. Palmprint Authentication A palmprint refers to an image acquired of the palm region of the hand. It can be either an online image (i.e. taken by a scanner) or offline image where the image is taken with ink and paper[4]. It differs from a fingerprint, in that it also contains other information such as texture, indents and marks which can be used when comparing one palm to another. The inner surface of the palm normally contains three flexion creases, secondary creases and ridges. The flexion creases are also called principal lines and the secondary creases are called wrinkles. The flexion and the major secondary creases are formed between the 3rd and 5th months of pregnancy and superficial lines appear after our birth, as shown in fig. 1. Although the three major flexions are genetically dependent, most of other creases are not. Even identical twins have different palmprints. These non-genetically deterministic and complex patterns are very useful in personal identification. Human beings have been interested in palm lines for fortune telling since long time. Pre-authentication: A database of Gabor-Palmprint features is prepared. Reference threshold values are also identified and stored in database. These values will be later used by Authentication system. Authentication: The authenticity of a person being Fig. 1: Inner surface of palm 3076

3 Palmprint research employs either high resolution or low resolution images. High resolution images are suitable for forensic applications such as criminal detection. Low resolution images are more suitable for civil and commercial applications such as access control. In general, high resolution refers to 400 dpi or more and low resolution refers to 150 dpi or less. Researchers can extract ridges, singular points and minutia points as features from high resolution images while in low resolution images they generally extract principal lines, wrinkles and texture[4],[5]. Initially palmprint research focused on high-resolution images but now almost all research is on low resolution images for civil and commercial applications. Automatic Palmprint identification systems can be classified into two categories: online and offline. An online system captures Palmprint images using a Palmprint capture sensor that is directly connected to a computer for real-time processing. An offline Palmprint identification system usually processes previously captured palmprint images which are often obtained from inked palmprint that were digitized by a digital scanner. Palm print authentication is a form of computer vision that uses palms to attempt to identify a person or verify a person s claimed identity. Regardless of specific method used, it is accomplished in a four step process. Fig. 2 shows the General Architecture of Palmprint Authentication System. Image input Palm Detection Image Processing Palm Authentication different methods to extract the identifying features of a palm. The most popular method is Gabor feature extraction method. Image processing: The next step is to compare the template generated in previous step with those in a database of known palms. In an identification application, this process yields scores that indicate how closely the generated template matches each of those in the database. In a verification application, the generated template is only compared with one template in the database. Palmprint authentication: The final step is determining whether any scores produced in above step are high enough to declare a match. The rules governing the declaration of a match are often configurable by the end user, so that he/she can determine how the palmprint authentication system should behave based on security and operational considerations. B. Gabor Wavelets The Gabor wavelets, which capture the properties of spatial localization, orientation selectivity, spatial frequency selectivity and quadrature phase relationship, seem to be a good approximation to the filter response profiles encountered experimentally in cortical neurons. The Gabor wavelets have been found to be particularly suitable for image decomposition and representation when the goal is the derivation of local and discriminating features. Researchers have experimentally shown that the Gabor filter representation gave better performance for classifying actions. In this section, we review the basics on Gabor wavelets, describe the Gabor feature representation of images and derive a Gabor feature vector. Database Gabor wavelets are used for image analysis because of their biological relevance and computational properties, optimally localized in the space and frequency domains. Here features of palmprint have been extracted using complex Gabor wavelet method. Fig. 2: General Architecture of Palmprint Authentication System Image acquisition: An image of palm is acquired. This acquisition can be accomplished by digitally scanning an existing photograph or by using an electro-optical camera to acquire live picture of the subject. The Gabor filter is basically a Gaussian (with variances sx and sy along x and y - axes respectively) modulated by a complex sinusoid described by the following equation: i =1, 2 (1) Palm detection: Once the palm detection software has targeted a palm, it can be analyzed with the spatial geometry of distinguishing features of the palm. Different vendors use 3077

4 First, the line information extracted is binarized by the following equation Description: I: Input image (3) sx, sy - variances along x and y axes respectively θ- Orientation of Gabor Wavelet G1 and G2 - output Gabor wavelets. The sample of Gabor wavelet convolution with the palmprint image is shown in Fig. 3. Where (i,j) = Complex Gabor wavelet features corresponding to nth orientation n=0, 1, 2.6 i, j rows and columns of the complex Gabor wavelet features. Hamming distance calculates the difference between two binary feature vectors using EX-OR operation and can be defined as (4) Where = hamming distance at orientation n, n= 0,1,2,..,6 Fig. 3: Feature extraction by Complex Gabor Wavelet i,j- rows and columns of the complex Gabor wavelet feature vector - Exclusive OR (EX-OR) operation FV - feature vector of the person to be matched - feature vector in database FV is same as CGWF D. Sliding Window Approach The hamming distance is compared by using sliding window method. Here the ROI is reduced by the window size (WS) and the window of (60-WS)x(60-WS) slides over the row and columns are 60X60 pixels considered for hamming distance[7]. The minimum value of the hamming distance values is considered Fig. 4: Complex Gabor wavelet features (CWGF) images The feature vector contains 7 orientations corresponding to each orientation (0, 30, 60, 90, 120, 150, 180) degrees. The feature vector is given by: CGWF= [CGWF0, CGWF1, CGWF2, CGWF3, CGWF4, CGWF5, CGWF6] (2) Gabor wavelet transform seems to be the optimal basis to extract local features. C. Hamming Distance Similarity The complex Gabor wavelet Line features vectors are matched by Hamming distance similarity measurement. Fig. 5: Sliding Window approach with Window Size 4 and Palmprint size 60X60 The modified Hamming distance value at n - orientation with window size is defined in 3078

5 (5) The average of all Hamming distances for n orientations is calculated using eqn (7) Where - Hamming distance with window size WS at nth orientation n=0,1,2 6 i,j- rows and columns of the complex Gabor wavelet feature vector - Exclusive OR (EX-OR) operation FV- the feature vector of the person to be matched - The feature vector in database The minimum out of 16(For window size WS=4, 4*4=16 values of hamming distance s is chosen as Hamming distance as calculated in eqn (6) n=0, 1, 2.6 V. FLOWCHART (7) (6) (a) Step1 (b) Step2 (c) Step3 (d) Step4 NO YES NO YES (e) Step5 (f) Step16 IMPOSTER Fig. 6: Various steps in sliding window method 3079

6 VI. IMPLEMENTATION AND RESULTS We have put efforts to implement this paper with Graphical User Interface (GUI) using the GUIDE Command of MATLAB. A training set consists of samples of palm images extracted from palm databases. These images include palms of different people and are used for both implementing the database and testing purposes. Then the testing images are compared against the database images for authentication process. The results obtained are summarized below. Fig. 9: When Load database is pressed and successfully database is loaded Fig. 7: User Interface with functionalities Fig. 10: When Authenticate is pressed Fig. 8: When Load database is pressed and the loading process is on Fig. 11: When selecting a test image for authentication 3080

7 REFERENCES Fig. 12: When the selected test image is authenticated [1] G. Shobha, M. Krishna and S.C. Sharma, Development of Palmprint Verification System Using Biometrics, Journal of Software, Vol.17, No.8, August 2006, pp [2] Ajay Kumara, David Zhang, Personal authentication using multiple palmprint representation, Pattern Recognition 38 (2005) [3] Jegoon Ryu and Sei-ichiro Kamata, Individual authentication through hand posture recognition using multi-hilbert scanning distance, 20th European Signal Processing Conference (EUSIPCO 2012) Bucharest, Romania, August 27-31, [4] S.M. Prasad, V.K. Govindan and P.S. Sathidevi, Image quality augmented intramodal palmprint Authentication, The Institution of Engineering and Technology 2012 [5] Hariprasath. S and Prabakar.T.N, Multimodal Biometric Recognition Using Iris Feature Extraction and Palmprint Features, IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012) March 30,31,2012 [6] Wei Li, Bob Zhang, Lei Zhang and Jingqi Yan, Principal Line-Based Alignment Refinement for Palmprint Recognition, IEEE Transactions on systems, MAN and Cybernetics Part C: Applications and reviews, VOL. 42, NO. 6, November 2012 [7] Aythami Morales, Miguel A. Ferrer and Ajay Kumar, Improved Palmprint Authentication using Contactless Imaging, IEEE 2010 [8] Jiansheng Chen and Yiu-Sang Moon, Using SIFT Features in Palmprint Authentication in IEEE 2008 [9] Xiang-Qian Wu, Kuan-Quan Wang and David Zhang, Fusion of multiple features for palmprint authentication, Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, August 2006 [10] Xiangqian Wu, Kuanquan Wang, Fengmiao Zhang and David Zhang, Fusion of Phase and Orientation Information for Palmprint Authentication, IEEE 2005 Fig. 13: When the selected test image is concluded as an imposter VII. CONCLUSION AND FUTURE SCOPE The Complex Gabor Wavelet transform is applied to each palmprint image in the database to get imaginary wavelet coefficients. The features are computed at different orientations. The feature vector generated from complex Gabor features are matched by Hamming distance using Sliding window method. Proposed Complex Gabor Wavelet method can discriminate palmprints and the use of Sliding window method makes it a fast authentication system. The Independent Gabor Feature method can be applied for colored images and also for moving images for palmprint authentication. It can be made more attractive by including palm detection and later recognizing the palm in a group of palms. Sadiya Thazeen, pursuing M.Tech from Rajarajeswari College of Engineering, Bangalore. She is involved in Biometry related technical research and interested in Digital Communication, Digital Signal Processing, Wireless Communication and Networking. Feroz Morab, final year M.Tech, Rajarajeswari College of Engineering, Bangalore. He is moving towards Ph.D and his areas of interest include Smart antennas, Communication, Field Theory and Digital Signal Processing. Mohammed Najmus Saqhib obtained Masters in Digital Electronics with high merit. He is currently working towards obtaining a Doctorate. His research areas are Communication and Networking. Seema Morab is pursuing her Ph.D from AMITY UNIVERSITY, Noida, Delhi NCR. 3081