An improved face recognition method using Local Binary Pattern method

Sheikh Ahmed Saleh; S. Azam; K.C. Yeo; B. Shanmugam; K. Kannoorpatti

doi:10.1109/ISCO.2017.7855964

An improved face recognition method using Local Binary Pattern method

Sheikh Ahmed Saleh, S. Azam, K.C. Yeo, B. Shanmugam, K. Kannoorpatti

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review

Abstract

Security system based on biometrics is becoming more popular everyday as a part of safety and security measurement against all kind of crimes. Among several kinds of biometric security systems, face recognition is one of the most popular one. It is one of the most accurate, mostly used recognition methods in modern world. In this paper, two most popular face recognition methods have been discussed and compared using average image on Yale database. To reduce calculation complexity, all training and test images are converted into gray scale images. The whole face recognition process can be divided into two parts face detection and face identification. For face detection part, Viola Jones face detection method has been used out of several face detection methods. After face detection, face is cropped from the actual image to remove the background and the resolution is set as 150×150 pixels. Eigenfaces and fisherfaces methods have been used for face identification part. Average images of subjects have been used as training set to improve the accuracy of identification. Both methods are investigated using MATLAB to find the better performance under average image condition. Accuracy and time consumption has been calculated using MATLAB code on Yale image database. In future, this paper will be helpful for further research on comparison of different face recognition methods using average images on different database.

Original language	English
Title of host publication	Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017
Editors	IEEE, Institute of Electrical and Electronics Engineers
Place of Publication	Coimbatore, India
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	112-118
Number of pages	7
Edition	1
ISBN (Electronic)	9781509027170
ISBN (Print)	9781509027187
DOIs	https://doi.org/10.1109/ISCO.2017.7855964
Publication status	Published - 2017
Event	2017 11th International Conference on Intelligent Systems and Control, ISCO 2017 - Coimbatore, India Duration: 5 Jan 2017 → 6 Jan 2017

Conference

Conference	2017 11th International Conference on Intelligent Systems and Control, ISCO 2017
Country/Territory	India
City	Coimbatore
Period	5/01/17 → 6/01/17

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10.1109/ISCO.2017.7855964

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Saleh, S. A., Azam, S., Yeo, K. C., Shanmugam, B., & Kannoorpatti, K. (2017). An improved face recognition method using Local Binary Pattern method. In IEEE. I. O. E. A. E. E. (Ed.), Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017 (1 ed., pp. 112-118). [16674850] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISCO.2017.7855964

Saleh, Sheikh Ahmed ; Azam, S. ; Yeo, K.C. et al. / An improved face recognition method using Local Binary Pattern method. Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017. editor / IEEE, Institute of Electrical and Electronics Engineers. 1. ed. Coimbatore, India : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 112-118

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title = "An improved face recognition method using Local Binary Pattern method",

abstract = "Security system based on biometrics is becoming more popular everyday as a part of safety and security measurement against all kind of crimes. Among several kinds of biometric security systems, face recognition is one of the most popular one. It is one of the most accurate, mostly used recognition methods in modern world. In this paper, two most popular face recognition methods have been discussed and compared using average image on Yale database. To reduce calculation complexity, all training and test images are converted into gray scale images. The whole face recognition process can be divided into two parts face detection and face identification. For face detection part, Viola Jones face detection method has been used out of several face detection methods. After face detection, face is cropped from the actual image to remove the background and the resolution is set as 150×150 pixels. Eigenfaces and fisherfaces methods have been used for face identification part. Average images of subjects have been used as training set to improve the accuracy of identification. Both methods are investigated using MATLAB to find the better performance under average image condition. Accuracy and time consumption has been calculated using MATLAB code on Yale image database. In future, this paper will be helpful for further research on comparison of different face recognition methods using average images on different database.",

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author = "Saleh, {Sheikh Ahmed} and S. Azam and K.C. Yeo and B. Shanmugam and K. Kannoorpatti",

year = "2017",

doi = "10.1109/ISCO.2017.7855964",

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Saleh, SA, Azam, S , Yeo, KC , Shanmugam, B & Kannoorpatti, K 2017, An improved face recognition method using Local Binary Pattern method. in IEEEIOEAEE (ed.), Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017. 1 edn, 16674850, IEEE, Institute of Electrical and Electronics Engineers, Coimbatore, India, pp. 112-118, 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017, Coimbatore, India, 5/01/17. https://doi.org/10.1109/ISCO.2017.7855964

An improved face recognition method using Local Binary Pattern method. / Saleh, Sheikh Ahmed; Azam, S.; Yeo, K.C. et al.

Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017. ed. / IEEE, Institute of Electrical and Electronics Engineers. 1. ed. Coimbatore, India : IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 112-118 16674850.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review

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AU - Yeo, K.C.

AU - Shanmugam, B.

AU - Kannoorpatti, K.

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AB - Security system based on biometrics is becoming more popular everyday as a part of safety and security measurement against all kind of crimes. Among several kinds of biometric security systems, face recognition is one of the most popular one. It is one of the most accurate, mostly used recognition methods in modern world. In this paper, two most popular face recognition methods have been discussed and compared using average image on Yale database. To reduce calculation complexity, all training and test images are converted into gray scale images. The whole face recognition process can be divided into two parts face detection and face identification. For face detection part, Viola Jones face detection method has been used out of several face detection methods. After face detection, face is cropped from the actual image to remove the background and the resolution is set as 150×150 pixels. Eigenfaces and fisherfaces methods have been used for face identification part. Average images of subjects have been used as training set to improve the accuracy of identification. Both methods are investigated using MATLAB to find the better performance under average image condition. Accuracy and time consumption has been calculated using MATLAB code on Yale image database. In future, this paper will be helpful for further research on comparison of different face recognition methods using average images on different database.

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BT - Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017

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Saleh SA, Azam S , Yeo KC , Shanmugam B , Kannoorpatti K. An improved face recognition method using Local Binary Pattern method. In IEEEIOEAEE, editor, Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017. 1 ed. Coimbatore, India: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 112-118. 16674850 https://doi.org/10.1109/ISCO.2017.7855964

An improved face recognition method using Local Binary Pattern method

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