Fingerprint Feature Extraction Based Discrete Cosine Transformation (DCT)

Transcription

1 Fingerprint Feature Extraction Based Discrete Cosine Transformation (DCT) Abstract- Fingerprint identification and verification are one of the most significant and reliable identification methods. It is impossible that two people have the same fingerprint. Automatics identification of humans based on fingerprint requires the input fingerprint to be match with a large number of fingerprints in the database. Generally, the fingerprint recognition systems are unable to solve the problem of rotated scanned input images. The classification systems are failed to classify the rotated scanned fingerprint image with the fingerprint image that store in the database, which both of the fingerprint images are actually belonging to the same person. In this paper, a simple and effectiveness algorithm is proposed for fingerprint image recognition and the proposed algorithm is able to solve the problem discussed above. The proposed algorithm involved two stages, which is pre-processing of fingerprint image and feature extraction based DCT. The extracted DCT data is used as input for the backpropagation neural network training for personal identification. I. INTRODUCTION Among various biometrics technologies, automatics fingerprint identification systems are the most popular and reliable for automatics personal identification system [1, 2, 3]. Fingerprint identification system has received increasing attention and the performance of fingerprint identification system has achieved a high level since year of 2000 due to the need of security in a large range of application [4, 5]. Many automatics fingerprint image recognition and classification methods are already available in the literature. Even many academic and commercial systems for fingerprint recognition exits, there is a necessity for further research in the topic in order to improve the reliability and performances of the fingerprint recognition system. Most of the fingerprint recognition system is based on the minutiae and feature extraction. The common extracted minutiae are ridge ending and bifurcation minutiae. This is because these two types of minutiae are found on most of the fingerprints. Generally, the fingerprint recognition systems are based on the matching of extracted ridge ending and bifurcation minutiae[6, 7, 8]. The extracted minutiae provided the information of position, types and quantities. Most of the matching process is done on the matching of the position and the total number of detected minutiae. Generally, the automatics fingerprint identification systems required the input fingerprint to be match with a large number of fingerprints in the database and it is time consuming if the matching processes used are the technique that discussed above [9, 10, 11]. In additional, the fingerprint recognition systems are unable to solve the problem of rotated input fingerprint image. The matching systems are failed to match the rotated input fingerprint image with the fingerprint image that stored in the database, which both of the fingerprint images are actually belonging to the same person. An effectiveness and efficient technique is required in order to solve the problem discussed above. In this paper, a simple feature extraction technique based DCT is proposed and the proposed technique is able to solve the problem of matching processes based rotated input fingerprint images. Further, the extracted DCT data are used as input for neural network training and this technique is able to reduce the computation and searching time for automatics fingerprint recognition. II. METHODOLOGY The fingerprint image is scanned with an optical fingerprint scanner. The scanned fingerprint image is saved in bitmap format with black and white colour. The scanned fingerprint image is then enhanced for quality improvement. Further, the enhanced fingerprint image is applied for binarization. The conversion is needed to reduce the computation and analyzing time for filtering and thinning process. The noise produced from the binarized fingerprint image is then removed using median filtering and the filtered fingerprint image is further thinned with a modified thinning algorithm proposed in [13, 14]. The bifurcation minutiae extraction method is further applied for the thinned fingerprint image. The extracted bifurcation minutiae is then applied for the feature extraction based DCT. The extracted feature data are then further used for neural network training. Fig. 1 shows the methodology overview. Fig. 1. Methodology overview.

2 III. PER-PROCESSING OF FINGERPRINT IMAGE The pre-processing of fingerprint image involved several stages, which are the processes of enhancement, binarization, filtering and thinning. The enhancement of the fingerprint image is made to adjust the brightness and the sharpness of the fingerprint image and also to improve the quality of the scanned fingerprint image. The binarization process is then applied to the enhanced fingerprint image. The binarization is needed in order to convert the range of the value from in the enhanced image to the range of 0 s and 1 s only. The conversion is also needed to reduce the computation and analyzing time for filtering and thinning process. The noise presented in the binary image is then filtered by using a median filter. Figs. 2, 3, 4 and 5 show the original scanned fingerprint image, the enhanced fingerprint image, the binary formatted fingerprint image and the filtered fingerprint image with respectively. Fig. 5. Filtered fingerprint image. The thinning process is the last process of the preprocessing of fingerprint image. In this paper, the thinning algorithm proposed by [13, 14] is used. The result after the filtered fingerprint image that applied to the thinning algorithm is shows in Fig. 6 below. Fig. 2. Original scanned fingerprint image. Fig. 3. Enhanced fingerprint image. Fig. 6. Thinned fingerprint image. IV. MINUTIAE AND FEATURE EXTRACTION The feature extraction is involved two sections, which is bifurcation minutiae extraction and feature extraction based DCT. First, the bifurcation minutiae is extracted, the extracted bifurcation minutiae provided the position and the quantity information. Then, the extracted bifurcation minutiae are arranged into counter clockwise direction. Further, the extracted fingerprint minutiae are applied to the DCT formula. A set of data is successfully collected after 100 fingerprint images are applied to the feature extraction discussed above. The extracted DCT data is further used as inputs for neural network training. The algorithms of the bifurcation minutiae extraction and feature extraction based DCT are listed as below with respectively. The following rules are applied in order to detect the Bifurcation minutiae: If B(P i ) 4 and A(P i ) 3 Let B(P i ) represents the number of binary pixel in the binary image with value 1 in 8-neighbor pixels and can be expressed as; B(P i ) = P 1 + P P 8 (1) Fig. 4. Binarized fingerprint image. and let A(P i ) represents the number of combination of 0 s 1 s pattern in 8-neighbor pixels. Fig. 7 shows the P 1, P 2, P 3,, P 8 position in a 3x3 mask.

3 Fig x 3 Mask pixel position. The values of B(P i ) and A(P i ) for some 8 neighbours pixel combinations are shown in the Fig. 8 and the possible bifurcation minutiae patterns is shows in Fig. 9. A(P i) = 3 A(P i) = 2 A(P i) = 2 A(P i) = 2 B(P i ) = 5 B(P i ) = 3 B(P i ) = 6 B(P i ) = 5 Fig. 8. (0 1) Connectivity of A(P i) and B(P i). Fig. 9. Bifurcation minutiae patterns. Algorithm of feature extraction based DCT. Start 1.0 Read extracted bifurcation minutiae data. 2.0 Arrange the bifurcation minutiae with counter clockwise direction. 3.0 Applied the arranged minutiae data into DCT formula. End V. NEURAL NETWORK ARCHITECTURE Backpropagation neural networks are widely used for classification, approximation, prediction, and control problems. Based on biological analogy, neural networks try to emulate the human brain s ability to learn from examples, learn from incomplete data and especially to generalize concepts. The aim of the neural network is to train the net to achieve a balance between the net s ability to respond and the net s ability to give reasonable responses to the input that is similar, but not identical to the one used in the training. A neural network is composed of a set of nodes connected by weights. The nodes are partitioned into three layers namely input layer, hidden layer and output layer. Backpropagation algorithm is most commonly used to derive the weights of the network. The weight adjustment is based on the generalized delta rule [12, 13, 14]. Fig. 10 below shows the architecture of the neural network. Input layer Hidden layer Output layer Fig. 10. Neural network architecture

4 VI. NEURAL NETWORK TRAINING A set of feature data is successfully extracted after the scanned fingerprint images applied for the features extraction methods that proposed in this paper. The extracted data consists of 20 neurons in the input layer, 25 neurons in hidden layer and 1 neuron for output layer. The input and hidden neurons have a bias value of 1.0 and is activated by the binary sigmoidal activation function. The initial weights are randomized between -0.5 and 0.5 and normalized. The training samples involved a set of 120 data, testing samples involved 110 data and the network is trained with the conventional backprogation neural network. The training result is shows in Table 1 below and the result plotted for cumulative error versus epoch is shows in Fig. 11 below. TABLE 1. FINGERPRINT FEATURE PATTERNS CLASSIFICATION, CONVENTIONAL BACKPROPAGATION Input neurons: 20 Hidden neurons: 25 Output neuron: 1 Learning rate: 0.2 Momentum factor: 0.85 Training tolerance: Testing tolerance: 0.1 Function: 1/( 1+e -x ) Training samples: 120 Testing samples: 110 Number of epoch: Bias: 1.0, 1.0 Failure: 0 Min epoch: Max epoch: Mean epoch: Standard deviation: Mean time (s): 2480 Max time (s): 7998 Mean time (s): 5786 Mean classification (%): 98.7 Fig. 11. Cumulative error versus epoch. VI. CONCLUSION In this paper, a pre-processing method and a feature extraction based DCT are proposed. The preprocessing steps are involved image enhancement, image binarization, image filtering and thinning process. The thinned fingerprint image is applied for feature extraction based DCT. The feature extraction based DCT is able to solve the problem of fingerprint image rotation. The extracted feature data are used for neural network training. The training results show that the classification of fingerprint image recognition is achieved an average of 98.7%. REFERENCES [1] Sagai, V.K; Koh Jit Beng,A.; Fingerprint Feature Extraction by fuzzy Logic and neural Network, Proceedings. ICONIP th International Conference on, Volume: 3, Nov Pages: vol. 3. [2] J.Soldek, V.Shmerko, Phil Philips, G.Kukharev, W.Rogers, and S.Yanushkevich, ; Image Analysis and Pattern Recognition in Biometric Technologies, Proceedings International Conference on The Biometrics: Fraud Prevention, Enchanced Service, Las Vegas, Nevada, USA, 1997, pp [3] Abu-Faraj, Z.; Abdallah, A.; Chebaklo, K.; Khoukaz, E. ; Fingerprint Identification Software for Forensic Applications ; Electronics, Circuits and Systems, ICECS The 7th IEEE International Conference on, Volume: 1, Dec. 2000, Pages: vol.1. [4] Tu Van Le, Ka Yeung Cheung, Minh Ha Nguyen,; A Fingerprint Recognizer Using Fuzzy Evolutionary Programming, Proceedings of The 34 th Hawaii International Conference on System Science [5] Bernard, S.; Boujemaa, N.; Vitale, D.; Bricot, C.; Fingerprint classification using Kohonen topologic map, Image Processing, Proceedings International Conference on, Volume: 3, 7-10 Oct. 2001, Pages: vol.3 [6] Virginia Espinosa-Duro, V; Fingerprints Thinning Algorithm, Aerospace and Electronic Systems Magazine, IEEE, Volume: 18, Issue: 9, Sept 2003, Pages: [7] Tucker, N.D; Pre Processing of Fingerprint Image, Security Technology, Proceedings Institute of Electrical and Electronics Engineers 29 th Annual 1995 International Carnahan Conference on, Oct. 1995, Pages: [8] Emiroglu, I.; Aklan, M. B.; Pre-Processing Of Fingerprint Images, Security and Detection, 1997, ECOS 97., European Conference, April Pages : [9] Jun-Sik Kwon; Jun-Woong Gi; Eung-Kwan Kang; Image Processing, Proceedings 2001 International Conference Volume: 3, 7-10 Oct. 2001, Pages: [10] Zahid Hussain, Digital Image Processing Thinning Algorithm, 1991, Ellis Horwood Limited, Pages: [11] Zaid Abu-faraj, and Kamal chebaklo, Fingerprint Identification Software for Forensic Applications, IEEE [12] Sivanandam, S.N., Paulraj, M., Introduction to Artificial Neural Network, Vikas Publication, New Delhi, (2003). [13] Paulraj M. Pandiyan, Sazali Yacoo, Azali Saudi and Chin Kim On, Certain Improvement in Fingerprint Recognition using Image Preprocessing, ADCOM 2004, 12 th International Conference on Advance Computing

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