Feature extraction and classification using deep convolutional neural networks, PCA and SVC for face recognition

Feature extraction and classification using deep convolutional neural networks, PCA and SVC for face recognition

Mohammed Kamel Benkaddour Abdennacer Bounoua  

Laboratory Communication Network & Architecture Multimedia RCAM DJILLALILIABBES University Sidi Bel Abbes 22000, Algeria

University Kasdi Marbah, FNTIC Faculty, Ouargla 30000, Algeria

Corresponding Author Email: 
benkaddour.kamel@univ-ouargla.dz
Page: 
77-91
|
DOI: 
https://doi.org/10.3166/TS.34.77-91
Received: 
|
Accepted: 
|
Published: 
30 June 2017
| Citation

ACCESS

Abstract: 

Recently, the facial recognition has aroused the interest of the scientific community, this technique of biometric that is effective, non-intrusive and contactless has taken an increasingly important part in the field of research. This paper proposes a face recognition and classification method based on deep learning, in particular Convolutional Neural Network (CNN), which are incredibly a powerful tools that have found great success in image classification and pattern recognition. In this work, the approach to this task is based on the Convolutional Neural Network (CNN) as a powerful feature extraction followed by Support Vector Machines (SVM) as a high classifier. To reduce the dimension of these features, a principal component analysis (PCA) technique is employed. We conduct an extensive evaluation of our methods on the FERET dataset. The results obtained showed that the proposed method CNN combine with PCA and Svc solution provide a significant improvement in performance and enhance the recognition accuracy.

Keywords: 

biometrics, face recognition, feature extraction, convolutional neural network, CNN, support vector machines (SVM), SVC, principal component analysis, PCA

1. Introduction
2. Proposed method
3. Datasets
4. Experimental work and results
5. Conclusion
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