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In Finger-Knuckle-Print (FKP) recognition, feature extraction plays a very important role in the overall system performance. This paper merges two types of the histograms of oriented gradients (HOG)-based features extracted from reflectance and illumination images for FKP-based identification. The Adaptive Single Scale Retinex (ASSR) algorithm has been used to extract the illumination and the reflectance images from each FKP image. Serial feature fusion is used to form a large feature vector for each user, and extract the distinctive features in the higher-dimension vector space. Finally, the cosine similarity distance measure is used for classification. The Hong Kong Polytechnic University (PolyU) FKP database is used during all of the tests. Experimental results show that our proposed system achieves better results than other state-of-the-art system.

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In this paper, a new method based on Log Gabor-TPLBP (LGTPLBP) has been proposed. However the Three Patch Local Binary Patterns (TPLBP) technique used in face recognition has been applied in Finger-Knuckle-Print (FKP) recognition. The 1D-Log Gabor filter has been used to extract the real and the imaginary images from each of the Region of Interest (ROI) of FKP images. Then the TPLBP descriptor on both images has been applied to extract the feature vectors of the real image and the imaginary image respectively. These feature vectors have been jointed to form a large feature vector for each image FKP. After that, the obtained feature vectors of all images are processed directly with a dimensionality reduction algorithm, using linear discriminant analysis (LDA). Finally, the cosine Mahalanobis distance (MAH) has been used for matching stage. To evaluate the effectiveness of the proposed system several experiments have been carried out. The Hong Kong Polytechnic University (PolyU) FKP database has been used during all of the tests. Experimental results show that the introduced system achieves better results than other stateof-the-art systems for both verification and identification.

Among several biometric systems presented in the literature, Finger Knuckle Print (FKP) authentication systems have received a great deal of attention in recent years. The present paper investigates a novel method in order to extract the optimal discriminant features from FKP images. This method use the 1D-Log Gabor filter, the Gabor filter bank and the Linear Discriminant Analysis (LDA). In the first step, the Region of Interest (ROI) of a FKP images is analysis with a 1D Log-Gabor wavelet to extract the preliminary feature which is presented by the real parts of the filtered image. In the second step, a Gabor filter bank is applied on the preliminary feature in order to selection only the discriminative features of FKP image. Finally, in the third step, the LDA technique is used to reduce the dimensionality of this feature and enhance its discriminatory power. Our biometric system is based on Nearest Neighbour classifier which uses the cosine Mahalanobis distance for the matching process. Experimental results showed that the proposed system achieves better results than other state-of-the-art systems.

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