Enhancing the performance of texture-based face recognition through multi-resolution techniques
Kanak Meena, A. Suruliandi, R. Reena Rose · International Journal of Biometrics · 2014
Automatic face recognition is an emerging active research area spanning several disciplines such as image processing, computer vision and pattern recognition. Face recognition is a challenging problem because of diversity in faces and variations caused by expressions, illuminations, pose, occlusion, aging and so on. In this paper, multi-resolution techniques are combined with texture features to mitigate the effect of facial variations. Multi-resolution techniques investigated in this paper are discrete wavelet transform (DWT), ridgelet, curvelet and contourlet. Texture features are extracted from these transforms by using local binary pattern (LBP), local texture pattern (LTP), local derivative pattern (LDP), local tetra patterns (LTrPs) and local derivative ternary pattern (LDTP). The proposed method is tested on JAFFE, ORL, Yale, Essex and Georgia Tech databases containing more than 4,000 face images. From the results, it is observed that, the combined approach of multi-resolution techniques with texture features enhances the face recognition rate. In particular, contourlet transform with LDTP perform better than the other techniques considered for investigation.