Score-level fusion of two-dimensional and three-dimensional palmprint for personal recognition systems

Mourad Chaa; Naceur-Eddine Boukezzoula; Abdelouahab Attia

doi:10.1117/1.JEI.26.1.013018

22 February 2017 Score-level fusion of two-dimensional and three-dimensional palmprint for personal recognition systems

Mourad Chaa, Naceur-Eddine Boukezzoula, Abdelouahab Attia

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 1, 013018 (February 2017). https://doi.org/10.1117/1.JEI.26.1.013018

Abstract

Two types of scores extracted from two-dimensional (2-D) and three-dimensional (3-D) palmprint for personal recognition systems are merged, introducing a local image descriptor for 2-D palmprint-based recognition systems, named bank of binarized statistical image features (B-BSIF). The main idea of B-BSIF is that the extracted histograms from the binarized statistical image features (BSIF) code images (the results of applying the different BSIF descriptor size with the length 12) are concatenated into one to produce a large feature vector. 3-D palmprint contains the depth information of the palm surface. The self-quotient image (SQI) algorithm is applied for reconstructing illumination-invariant 3-D palmprint images. To extract discriminative Gabor features from SQI images, Gabor wavelets are defined and used. Indeed, the dimensionality reduction methods have shown their ability in biometrics systems. Given this, a principal component analysis (PCA)+linear discriminant analysis (LDA) technique is employed. For the matching process, the cosine Mahalanobis distance is applied. Extensive experiments were conducted on a 2-D and 3-D palmprint database with 10,400 range images from 260 individuals. Then, a comparison was made between the proposed algorithm and other existing methods in the literature. Results clearly show that the proposed framework provides a higher correct recognition rate. Furthermore, the best results were obtained by merging the score of B-BSIF descriptor with the score of the SQI+Gabor wavelets+PCA+LDA method, yielding an equal error rate of 0.00% and a recognition rate of rank-1=100.00%.

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Mourad Chaa, Naceur-Eddine Boukezzoula, and Abdelouahab Attia "Score-level fusion of two-dimensional and three-dimensional palmprint for personal recognition systems," Journal of Electronic Imaging 26(1), 013018 (22 February 2017). https://doi.org/10.1117/1.JEI.26.1.013018

Received: 27 August 2016; Accepted: 2 February 2017; Published: 22 February 2017

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