Many of the applications used to recognize humans are based on fingerprints. Fingerprint recognition is the most popular biometric technique widely used for person identification. This paper proposes a fingerprint rec...
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Many of the applications used to recognize humans are based on fingerprints. Fingerprint recognition is the most popular biometric technique widely used for person identification. This paper proposes a fingerprint recognition technique which uses the linear binary patterns for fingerprint representation and matching. An entire fingerprint image is divided into 9 equal sized zones. In each zone the linear binary patterns are identified and used for recognition. Neural network and Euclidean distance similarity measures are used for recognition. The proposed method is experimented using eight databases, comprising of 3500 samples in total. On an average accuracy of 94.28% and 91.15% are obtained for neural network and nearest neighbour classifiers respectively. (C) 2015 The Authors. Published by Elsevier B.V.
Many of the applications used to recognize humans are based on fingerprints. Fingerprint recognition is the most popular biometric technique widely used for person identification. This paper proposes a fingerprint rec...
详细信息
Many of the applications used to recognize humans are based on fingerprints. Fingerprint recognition is the most popular biometric technique widely used for person identification. This paper proposes a fingerprint recognition technique which uses the linear binary patterns for fingerprint representation and matching. An entire fingerprint image is divided into 9 equal sized zones. In each zone the linear binary patterns are identified and used for recognition. Neural network and Euclidean distance similarity measures are used for recognition. The proposed method is experimented using eight databases, comprising of 3500 samples in total. On an average accuracy of 94.28% and 91.15% are obtained for neural network and nearest neighbour classifiers respectively.
In this paper, we present the architecture of a smart imaging sensor (SIS) for face recognition, based on a custom-design smart pixel capable of computing local spatial gradients in the analog domain, and a digital co...
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In this paper, we present the architecture of a smart imaging sensor (SIS) for face recognition, based on a custom-design smart pixel capable of computing local spatial gradients in the analog domain, and a digital coprocessor that performs image classification. The SIS uses spatial gradients to compute a lightweight version of local binarypatterns (LBP), which we term ringed LBP (RLBP). Our face recognition method, which is based on Ahonen's algorithm, operates in three stages: (1) it extracts local image features using RLBP, (2) it computes a feature vector using RLBP histograms, (3) it projects the vector onto a subspace that maximizes class separation and classifies the image using a nearest neighbor criterion. We designed the smart pixel using the TSMC 0.35 mu m mixed-signal CMOS process, and evaluated its performance using postlayout parasitic extraction. We also designed and implemented the digital coprocessor on a Xilinx XC7Z020 field-programmable gate array. The smart pixel achieves a fill factor of 34% on the 0.35 mu m process and 76% on a 0.18 mu m process with 32 mu m x 32 mu m pixels. The pixel array operates at up to 556 frames per second. The digital coprocessor achieves 96.5% classification accuracy on a database of infrared face images, can classify a 150x80-pixel image in 94 mu s, and consumes 71 mW of power.
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