Authors: Deepti Ahlawat; Vijay Nehra
Addresses: Department of Electronics and Communication Engineering, Bhagat Phool Singh Mahila Vishwavidyalaya, Khanpur Kalan, Sonipat, India ' Department of Electronics and Communication Engineering, Bhagat Phool Singh Mahila Vishwavidyalaya, Khanpur Kalan, Sonipat, India
Abstract: In this investigation, an efficient hybrid approach involving phase congruency (PC) and shift invariant feature transform (SIFT) for face recognition is presented. The present study exploits the advantages of PC and SIFT together for the purpose of efficient feature extraction for the facial images. The effectiveness of the present work is analysed and compared using other classifiers, i.e. K-means and self-organizing map. The results of this study demonstrate that phase congruency - shift invariant feature transform is robust to expression variations and shows better performance than other comparative methods and achieves good recognition accuracy. Studies are conducted on Japanese female facial expression and Yale databases. The proposed technique has been compared with the existing techniques, and from the experiments, it is observed that the results of the proposed technique are better than the existing techniques.
Keywords: adaboost; phase congruency; shift invariant feature transform; support vector machine.
International Journal of Signal and Imaging Systems Engineering, 2017 Vol.10 No.5, pp.237 - 247
Accepted: 26 Jun 2017
Published online: 01 Nov 2017 *