An Improved Face Recognition Algorithm Based on Sparse Representation

Cemil Turan; Shirali Kadyrov; Diana Burissova

doi:10.1109/CoCoNet.2018.8476916

An Improved Face Recognition Algorithm Based on Sparse Representation

Cemil Turan, Shirali Kadyrov, Diana Burissova

Department of Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

This paper considers a variation of Sparse Representation-based Classification algorithm. Accuracy and time of evaluation of face recognition are two key performance indicators. This work compares performance of modified Sparse Representation-based Classification algorithm against original Sparse Representation-based Classification algorithm. Yale Face Database B is used to carry MATLAB simulations and results show that modified Sparse Representation-based Classification algorithm outperforms in terms of time. Moreover, the authors study and compare these algorithms when there is only a few training samples per subject is available.

Original language	English
Title of host publication	Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	32-35
Number of pages	4
ISBN (Electronic)	9781538659281
DOIs	https://doi.org/10.1109/CoCoNet.2018.8476916
Publication status	Published - Sep 28 2018
Event	2nd International Conference on Computing and Network Communications, CoCoNet 2018 - Astana, Kazakhstan Duration: Aug 15 2018 → Aug 17 2018

Conference

Conference	2nd International Conference on Computing and Network Communications, CoCoNet 2018
Country	Kazakhstan
City	Astana
Period	8/15/18 → 8/17/18

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Electrical and Electronic Engineering

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An Improved Face Recognition Algorithm Based on Sparse Representation. / Turan, Cemil; Kadyrov, Shirali; Burissova, Diana.

Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 32-35 8476916.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Turan, C, Kadyrov, S & Burissova, D 2018, An Improved Face Recognition Algorithm Based on Sparse Representation. in Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018., 8476916, Institute of Electrical and Electronics Engineers Inc., pp. 32-35, 2nd International Conference on Computing and Network Communications, CoCoNet 2018, Astana, Kazakhstan, 8/15/18. https://doi.org/10.1109/CoCoNet.2018.8476916

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abstract = "This paper considers a variation of Sparse Representation-based Classification algorithm. Accuracy and time of evaluation of face recognition are two key performance indicators. This work compares performance of modified Sparse Representation-based Classification algorithm against original Sparse Representation-based Classification algorithm. Yale Face Database B is used to carry MATLAB simulations and results show that modified Sparse Representation-based Classification algorithm outperforms in terms of time. Moreover, the authors study and compare these algorithms when there is only a few training samples per subject is available.",

author = "Cemil Turan and Shirali Kadyrov and Diana Burissova",

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N2 - This paper considers a variation of Sparse Representation-based Classification algorithm. Accuracy and time of evaluation of face recognition are two key performance indicators. This work compares performance of modified Sparse Representation-based Classification algorithm against original Sparse Representation-based Classification algorithm. Yale Face Database B is used to carry MATLAB simulations and results show that modified Sparse Representation-based Classification algorithm outperforms in terms of time. Moreover, the authors study and compare these algorithms when there is only a few training samples per subject is available.

AB - This paper considers a variation of Sparse Representation-based Classification algorithm. Accuracy and time of evaluation of face recognition are two key performance indicators. This work compares performance of modified Sparse Representation-based Classification algorithm against original Sparse Representation-based Classification algorithm. Yale Face Database B is used to carry MATLAB simulations and results show that modified Sparse Representation-based Classification algorithm outperforms in terms of time. Moreover, the authors study and compare these algorithms when there is only a few training samples per subject is available.

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An Improved Face Recognition Algorithm Based on Sparse Representation

Abstract

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ASJC Scopus subject areas

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