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Bouziane, Abderraouf
- Multimodal Finger Dorsal Knuckle Major and Minor Print Recognition System based on Pcanet Deep Learning
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Authors
Affiliations
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University, DZ
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University, DZ
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 3 (2020), Pagination: 2153-2158Abstract
Hand-based recognition systems with different traits are widely used techniques and are trustworthy ones. We can find it in different real life fields such as banks and industries due to its stability, reliability, acceptability, and the wide range features. In this paper, we present a finger dorsal knuckle print multimodal recognition system, where we use PCAnet (principal component analysis network) deep learning to extract the features from both Major and Minor finger dorsal knuckles to allow a deeper insight into the exploited trait. Then SVM is used for the matching stage of the two modalities, followed by a score level fusion method to combine the scores using different rules. In order to establish the effectiveness of the proposed system, several experiments in the course of this work have been done on the finger knuckle images of the publicly available database PolyUKV1. The results show that the proposed method has better results in comparison with a unimodal system.Keywords
Finger Knuckle Print, Major, Minor, PCAnet, Score Level Fusion, SVM.
References
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- Arabic Handwritten Characters Recognition Via Multi-Scale Hog Features and Multi-Layer Deep Rule-Based Classification
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Authors
Affiliations
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Department of Computer Science, University of Memphis, US
3 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj, DZ
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 Department of Computer Science, University of Memphis, US
3 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj, DZ
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 4 (2020), Pagination: 2195-2200Abstract
Optical character recognition systems for handwritten Arabic language still face challenges, owing to high level of ambiguity, complexity and tremendous variations in human writing styles. In this paper, we propose a new and effective Arabic handwritten characters recognition framework using multi-scale histogram oriented gradient (HOG) features and the deep rule-based classifier (DRB). In the feature extraction stage, the proposed framework combines multi-scale HOG features, and then the DRB is applied on comprehensive HOG features to obtain the final classification label/class. This study involves experimental analyses that were conducted on the publicly available cursive Arabic Handwritten Characters Database (AHCD) containing 16800 characters. Experimental results demonstrate the efficacy of the proposed recognition system compared to the existing state-of-the-art-systems.Keywords
Arabic Character Recognition, Writing, DRB Classifier, HOG, AHCD.References
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- Block Wise 3D Palmprint Recognition Based on Tan and Triggs with BSIF Descriptor
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Authors
Affiliations
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
1 Department of Computer Science, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
2 LMSE Laboratory, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, DZ
Source
ICTACT Journal on Soft Computing, Vol 11, No 2 (2021), Pagination: 2251-2259Abstract
Faced by problems such as lack of robustness from 2D palmprint recognition system which can result to be attacked using a fake palmprint or having the same palmprint as another individual, 3D can present an alternative solution to deal with this problem, hence in this paper we are going to introduce a novel approach based on 3D palmprint recognition system named TT-P-BSIF: first, a preprocessing technique based on Tan and Triggs method was applied on a 3D depth image in order to effectively and efficiently eliminate the effect of low frequency component and at the same time keeping the local statistical properties of the treated image. Then the processed image is divided into a regular number of blocks using two parameters (a and b), after that the Binarized Statistical local features (BSIF) has been applied on each block in order to extract the features vector. These vectors are all combined to produce one larger vector for each processed image. Afterwards nearest neighbor classifier is used to classifier the 3D palmprint images. To examine the proposed method, this latter has been evaluated on a 3D palmprint database that contains 8,000 samples, the obtained results were consistent and promising which proves that the introduced method can massively and effectively improve the recognition results. Therefore, this proposed work using Tan and Triggs method for preprocessing and BSIF for feature extraction was able to generate a recognition rate up to 99.63% and verification rate at 1% up to 100% with EER equals to 0.12%.Keywords
3D Palmprint, Tan and Triggs, BSIF, Nearest Neighbor Classifier.References
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