ICTACT Journal on Image and Video Processing

Abdelouahab Attia ¹, Abdelouahab Moussaoui ², Mourad Chaa ³, Youssef Chahir ⁴

Affiliations
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou, Arreridj, DZ
2 Department of Computer Science, Ferhat Abbas University, DZ
3 Department of New Technologies of Information and Communication, Ouargla University, DZ
4 Department of Computer Science, University of Caen Lower, FR

Abstract

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 state-of-the-art systems for both verification and identification.

Keywords

Biometric Systems, Three Patch Local Binary Patterns, 1D Log Gabor Filter, Finger Knuckle Print.

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Mourad Chaa ¹

Affiliations
1 Department of New Technologies of Information and Communication, Ouargla University, DZ

Abstract

This paper proposes a new local image descriptor for Finger Knuckle Print Recognition Systems (FKPRS), named Log-Gabor Binarized Statistical Image Features descriptor (LGBSIF). The idea of LGBSIF is based on the image Log-Gabor wavelet representation and the Binarized Statistical Image Features (BSIF). Initially, the Region of Interest (ROI) of the FKP images are analyzed with a 1D Log-Gabor wavelet to extract the preliminary features that are presented by both the real and imaginary parts of the filtered image. The main motive of the LGBSIF is to enhance the Log-Gabor real and imaginary features by applying the BSIF coding method. Secondly, histograms extracted from the encoded real and imaginary images respectively are concatenated in one large feature vector. Thirdly, the PCA+LDA technique is used to reduce the dimensionality of this feature and enhance its discriminatory power. Finally, the Nearest Neighbor Classifier that uses the Cosine distance is employed for the matching process. The evaluation of the performance of the proposed system is done on the Poly-U FKP database. However, the experimental results have shown that the proposed system achieves better results than other state-of-the-art systems and confirmed the tenacity of the proposed descriptor. Further, the results also prove that the performance efficiency of the introduced system in terms of recognition rate (Rank1) and equal error rate (EER) are 100% and 0% for both modes of identification and verification respectively.

Keywords

Biometric, Local Descriptor, Wavelet, Dimensionality Reduction, Classification.

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Abdelouahab Attia ¹, Abdelouahab Moussaoui ², Youssef Chahir ³, Mourad Chaa ⁴

Affiliations
1 Computer Science Department, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Computer Science Department, Ferhat Abbas University, DZ
3 Department of Computer Science, University of Caen, FR
4 Ouargla University, DZ

Abstract

3D Palmprint recognition has become a promising alternative tool for resolving problems compared to the robustness of 2D palmprint recognition. Regarding robustness, biometric systems that use 2D Palmprint suffer from being attacked by using a fake Palmprint identical. Given this, the current paper introduces a new 3D Palmprint recognition approach. Firstly, a set of preprocessing techniques has been applied on 3D depth image such as Tan and Triggs method which can effectively and efficiently eliminate the effect of the low-frequency component with keeping the local statistical properties of the processed image. Then, Gabor wavelets have been employed to extract features. After that, the extracted features have been used as an input in the collaborative representation based classification with regularized least squares (CRC_RLS) to classify the 3D Palmprint images. To evaluate its performance, the proposed algorithm has been applied on the PolyU 3D Palmprint database which contains 8.000 samples. The experimental results successfully and greatly improve the recognition results, especially when, we use Tan and Triggs method for preprocessing and Gabor for feature extraction with CRC_RLS for presentation and classification. We achieve a significant recognition rate of 100 % in lowest Runtime which reflects the robustness of the proposed recognition system.

Keywords

Three-Dimensional Palmprint, Biometric, Gaussian Difference Filtering, Gradient Palms, Weberpalms, Gabor Features, Self-Quotient Image Algorithm.

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