The 3rd International Conference on Next Generation Computing(ICNGC2017b)
Fingerprint matching and Anti-spoofing using hashing and CNN 1
Sadia , 1Sana, 1Muhammad Shahid, 2Tanveer Hussain
1 Institute of Business Management Sciences, University of Agriculture Peshawar Digital Image processing Lab, Dept. of Computer Science, Islamia College Peshawar, Pakistan
[email protected],
[email protected],
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[email protected] 2
Abstract security of information become an important issue which attracts many researchers toward it. In this aspect, many systems were introduced to ensure the privacy of data and information. In this paper we present a novel technique making use of biometric system to ensure the confidentiality of data. The proposed system acquires a fingerprint image as an input from the user using fingerprint reader and compares the hash generated from figure print with
proposed method done Minutiae extraction followed by secondary feature matching and then performing brute force match. Wahab et al.[2] proposed a technique in which some preprocessing techniques like histogram equalization, extraction and modification of ridge direction has been done in first step and after that two basic feature of the image, ridge endings and bifurcations are extracted which were then forwarded to matching phase to validate user identification. The graph of techniques based on feature matching or minutiae extraction high but These techniques are computationally expensive which also results in high time complexity. Our proposed technique uses a very simple and efficient technique for fingerprint matching called Hashing technique. Fingerprint matching is done by matching hashes generated by fingerprint. After successful match of fingerprint it is mandatory to check whether the fingerprint matched is spoof or real. Spoof fingerprint is formed by showing molds to outwit the biometric system. To overcome these problems different techniques have been used by researchers. For instance, using texture based approach, Maatta et al.[3] used multi-scale binary patterns for analysis of spoof attacks. But with the advancement of the technology, high quality images can gain illegitimate access by using biometric system. So under consideration of these limitations we proposed a scheme for fingerprint matching and also spoof detection. The recent success of the deep convolutional neural networks (CNNs) in the field of computer vision, has attracted researchers to utilize these multi-layer end-to-end learning architectures to perform a variety of tasks. Conventional methods use hand-crafted features followed by classifier training for solving anti-spoofing problems while CNN uses high level features. Xu et al. [4] presented a CNN architecture for spoofing attacks by putting long-short-term memory (LSTM) layer over the fully connected layers for feature extraction. Considering these motivations, we have used CNN in our framework for spoof detection and perceptual hashing for fingerprint matching.
After successful match, in the second stage, it is checked that either the user is spoofing or not by utilizing CNN based trained model. Preliminary results show very good accuracy of the proposed technique. Fingerprint; Spoof; Convolution Neural Network CNN; Hash. I. Introduction Finger print is actually an impression left by the ridges of a human finger. Fingerprint from past decade has gained a lot of importance due to the secrecy of information. Numbers of Applications based on fingerprint are present nowadays ranging from simple applications to complex applications. Some of these areas are voter registration and identification, border control via professional ID card verification with fingerprint recognition. There are a lot of challenges faced by biometric systems including fingerprint. First challenging task in fingerprint biometric system is to retrieve the accurate fingerprint from the database. Another challenge in this area is, spoof attacks. To overcome these challenges a novel technique is proposed in this paper. In last decade, numbers of schemes have been proposed in the field of fingerprint based authentication. Jea et al.[1] in their
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The 3rd International Conference on Next Generation Computing(ICNGC2017b)
Fig 1: Proposed Framework
hash acts just like fingerprint of the image and every different input image has its unique hash based on the features. For fingerprint matching we used hamming distance formula. Query fingerprint image is obtained through the fingerprint reader. Passing the image through hash function, 256-bit hash of the image is obtained. In the local database, fingerprint records of the employees of any organization is stored in the form of hashes. Hash attained from the function is compared with the hashes in the database and the hamming distance between the query hash and database hashes is stored. Distance between two hashes ranges from 0 to 1. Threshold selected for matching criteria is 0.8, any hash having similarity greater or equal to 0.8 is marked as registered fingerprint and is processed to the next process. Successful authentication of fingerprint invokes the spoof detector classifier trained on LivDet 2013 [5] dataset through AlexNet model [6]. Fake fingerprints can be obtained by pushing the finger into plastic like material and creating fake mark of fingerprint as mold, which is then filled with material like gelatin or silicone to reproduce the same but false fingerprint characteristics. By passing the fingerprint through the classifier, it classifies the fingerprint as spoof or real. In case of real fingerprint the user is granted access and in reverse access is denied.
II. Proposed Method The entire proposed framework given in Figure 1, is divided into three main steps. In first step, input is given as a fingerprint image using fingerprint reader, second step involves the authentication of fingerprint image using matching hashes, if the hash-match become successful, the input will assign to third step for fingerprint spoof detection. After acquiring input image from the first step of the process, it is converted into 256-bit hash. This hash is compared with database of already inserted hashes of employees of the organization or students of school depending upon the scenario. Upon successful match of the fingerprint the process is further pushed to the third step, which is detection of spoof fingerprint. If the training classifier of the proposed system approves the fingerprint as spoof, person is considered as un-authorized to the system. Hashing technique followed in the proposed method include the following steps. Image resizing; proposed method resizes the image to 256 by 256 pixels to get 256-bit hash. In the next step threshold is selected on the base of mean average calculation from the pixel values of the image. Hash is calculated by iterating through the image and assigning 1 to the pixel having value greater than the threshold calculated and 0 to the others. Final hash is acquired by converting the binary values into decimal form, which is 256 by 1 column vector. This 256-bit
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The 3rd International Conference on Next Generation Computing(ICNGC2017b)
Fig 2: Perceptual Hashing Technique [2]
III. Conclusion IV. Initial results of the proposed system prove it very efficient and accurate for any system. Perceptual hashing technique in this paper is used to find the hash of fingerprints. For matching of fingerprint, hamming distance algorithm is used. Spoofing model of the proposed system is trained using CNN. Proposed technique has accuracy of 97.5 % over LivDet 2013 [5] on spoofing CNN classifier and has 96 % accuracy of authentication over local fingerprint database.
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References [1]
Jea, T.-Y. and V. Govindaraju, A minutia-based partial fingerprint recognition system. Pattern Recognition, 2005. 38(10): p. 1672-1684.
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Wahab, A., S. Chin, and E. Tan, Novel approach to automated fingerprint recognition. IEE Proceedings-Vision, Image and Signal Processing, 1998. 145(3): p. 160-166. Määttä, J., A. Hadid, and M. Pietikäinen, Face spoofing detection from single images using texture and local shape analysis. IET biometrics, 2012. 1(1): p. 3-10. Xu, Z., S. Li, and W. Deng. Learning temporal features using LSTMCNN architecture for face anti-spoofing. in Pattern Recognition (ACPR), 2015 3rd IAPR Asian Conference on. 2015. IEEE. Ghiani, L., et al. Livdet 2013 fingerprint liveness detection competition 2013. in Biometrics (ICB), 2013 International Conference on. 2013. IEEE. Szegedy, C., et al. Going deeper with convolutions. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
