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An improved technique for face recognition applications

An improved technique for face recognition applications The face recognition problem has a long history and a significant practical perspective and one of the practical applications of the theory of pattern recognition, to automatically localize the face in the image and, if necessary, identify the person in the face. Interests in the procedures underlying the process of localization and individual’s recognition are quite significant in connection with the variety of their practical application in such areas as security systems, verification, forensic expertise, teleconferences, computer games, etc. This paper aims to recognize facial images efficiently. An averaged-feature based technique is proposed to reduce the dimensions of the multi-expression facial features. The classifier model is generated using a supervised learning algorithm called a back-propagation neural network (BPNN), implemented on a MatLab R2017. The recognition rate and accuracy of the proposed methodology is comparable with other methods such as the principle component analysis and linear discriminant analysis with the same data set. In total, 150 faces subjects are selected from the Olivetti Research Laboratory (ORL) data set, resulting 95.6 and 85 per cent recognition rate and accuracy, respectively, and 165 faces subjects from the Yale data set, resulting 95.5 and 84.4 per cent recognition rate and accuracy, respectively.Design/methodology/approachAveraged-feature based approach (dimension reduction) and BPNN (generate supervised classifier).FindingsThe recognition rate is 95.6 per cent and recognition accuracy is 85 per cent for the ORL data set, whereas the recognition rate is 95.5 per cent and recognition accuracy is 84.4 per cent for the Yale data set.Originality/valueAveraged-feature based method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Information and Learning Science Emerald Publishing

An improved technique for face recognition applications

Zaqout, Ihab; Al-Hanjori, Mones
Information and Learning Science , Volume 119 (9/10): 16 – Nov 13, 2018
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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2398-5348
DOI
10.1108/ils-03-2018-0023
Publisher site
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Abstract

The face recognition problem has a long history and a significant practical perspective and one of the practical applications of the theory of pattern recognition, to automatically localize the face in the image and, if necessary, identify the person in the face. Interests in the procedures underlying the process of localization and individual’s recognition are quite significant in connection with the variety of their practical application in such areas as security systems, verification, forensic expertise, teleconferences, computer games, etc. This paper aims to recognize facial images efficiently. An averaged-feature based technique is proposed to reduce the dimensions of the multi-expression facial features. The classifier model is generated using a supervised learning algorithm called a back-propagation neural network (BPNN), implemented on a MatLab R2017. The recognition rate and accuracy of the proposed methodology is comparable with other methods such as the principle component analysis and linear discriminant analysis with the same data set. In total, 150 faces subjects are selected from the Olivetti Research Laboratory (ORL) data set, resulting 95.6 and 85 per cent recognition rate and accuracy, respectively, and 165 faces subjects from the Yale data set, resulting 95.5 and 84.4 per cent recognition rate and accuracy, respectively.Design/methodology/approachAveraged-feature based approach (dimension reduction) and BPNN (generate supervised classifier).FindingsThe recognition rate is 95.6 per cent and recognition accuracy is 85 per cent for the ORL data set, whereas the recognition rate is 95.5 per cent and recognition accuracy is 84.4 per cent for the Yale data set.Originality/valueAveraged-feature based method.

Journal

Information and Learning Science – Emerald Publishing

Published: Nov 13, 2018

Keywords: Classification; Back propagation neural network; Face recognition; ORL dataset; Supervised learning; Yale dataset

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