Face recognition using transform domain texture features

Rangaswamy Y.; Ramya S K; K B Raja; Venugopal K. R.; L. M. Patnaik

doi:10.1117/12.2051957

24 December 2013 Face recognition using transform domain texture features

Rangaswamy Y., Ramya S K, K B Raja, Venugopal K. R., L. M. Patnaik

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90671P (2013) https://doi.org/10.1117/12.2051957
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

Abstract

The face recognition is an efficient biometric system to identify a person. In this paper, we propose Face Recognition using Transform Domain Texture Features (FRTDTF). The face images are preprocessed and two sets of texture features are extracted. In first feature set, the Discrete Wavelet Transform (DWT) is applied on face image and considered only high frequency sub band coefficients to extract edge information efficiently. The Dual Tree Complex Wavelet Transform (DTCWT) is applied on high frequency sub bands of DWT to derive Low and High frequency DTCWT coefficients. The texture features of DTCWT coefficients are computed using Overlapping Local Binary Pattern (OLBP) to generate feature set 1. In second feature set, the DTCWT is applied on preprocessed face image and considered all frequency sub bands coefficients to extract significant information and edge information of face image. The texture features of DTCWT matrix are computed using OLBP to generate feature set 2. The final feature set is the concatenation of feature set 1 and set 2. The Euclidian distance (ED) is used to compare test image features with features of face images in the database. It is observed that, the performance parameter values are better in the case of proposed algorithm compared to existing algorithms.

Citation Download Citation

Rangaswamy Y., Ramya S K, K B Raja, Venugopal K. R., and L. M. Patnaik "Face recognition using transform domain texture features", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90671P (24 December 2013); https://doi.org/10.1117/12.2051957

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