Enhancing Face Recognition Performance using Triplet Half Band Wavelet Filter Bank

Abstract

Abstract-Face recognition using subspace methods are quite popular in research community. This paper proposes an efficient face recognition method based on the application of recently developed triplet half band wavelet filter bank (TWFB) as pre-processing step to further enhance the performance of well known linear and nonlinear subspace methods such as principle component analysis(PCA),kernel principle component analysis (KPCA), linear discriminant analysis (LDA), and kernel discriminant analysis (KDA). The design of 6 th order TWFB is used as the multiresolution analysis tool to perform the 2-D discrete wavelet transform (DWT). Experimental results are performed on two standard databases ORL and Yale. Comparative results are obtained in terms of verification performance parameters such as false acceptance rate (FAR), false rejection rate (FRR) and genuine acceptance rate (GAR). Application of TWFB enhances the performance of PCA, KPCA, LDA, and KDA based methods.

Score Fusion of SIFT & SURF Descriptors for Face Recognition Using Wavelet Transforms

“…Also, it has been described as one of the best applications of image processing and analysis [2]. Different statistical methods and algorithms such as Principal Component Analysis or Eigenface (PCA) [3], Local Binary Pattern (LBP) [4], Independent Component Analysis (ICA) [5], and triplet half band wavelet filter bank (TWFB) [6] algorithms have been developed for face recognition purposes. In [7] Speed-Up Robust Feature (SURF) and Linear Discriminant Analysis (LDA) are used to improve the quality parameters of face recognition and optimizing the result.…”