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Attia, Abdelouahab
- 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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- User Activities Analysis in Location Based Social Network Via Association Rules
Abstract Views :193 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, Ferhat Abbas University, DZ
2 Department of Computer Science, Mohamed El-Bachir Ibrahimi University of Bordj Bou Arreridj, DZ
3 State University of New York Polytechnic Institute, US
1 Department of Computer Science, Ferhat Abbas University, DZ
2 Department of Computer Science, Mohamed El-Bachir Ibrahimi University of Bordj Bou Arreridj, DZ
3 State University of New York Polytechnic Institute, US
Source
ICTACT Journal on Soft Computing, Vol 11, No 3 (2021), Pagination: 2328-2336Abstract
In recent years, the field of the Internet of Things (IoT), including smart and wearable devices, has witnessed a tremendous advancement leading to the collection of a wide variety of information not only about users but also their activities via various systems such as social networks, apps and so on. Thus, the collection of this large amount of data allows social systems to reach a wide variety of targets and gives more visibility about users and their profiles. It can also help to improve the services and functionalities of the users. Besides, the analysis and prediction of user’s activities in location-based social networks (LBSNs) have received much attention both from industries and research communities, especially in smart city developments, which give much importance to the automation of the LBSNs. In this paper, we present a new method based on association rules for user activity analysis in LBSNs. In particular, the Apriori algorithm has been applied to extract the consequential and advantageous rules to categorize users’ profiles. Empirical evaluations on a publicly available large-scale real-world dataset, named Gowalla, demonstrate the effectiveness of the presented association rules-based system in analyzing users’ activities via LBSNs.Keywords
Complex System, Social Networks, Association Rules, Apriori Algorithm, Gowalla Dataset.References
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