Face Recognition Based on Wavelet Kernel Non-Negative Matrix Factorization

Abstract

In this paper a novel face recognition algorithm, based on wavelet kernel non-negative matrix factorization (WKNMF), is proposed. By utilizing features from multi-resolution analysis, the nonlinear mapping capability of kernel nonnegative matrix factorization could be improved by the method proposed. The proposed face recognition method combines wavelet kernel non-negative matrix factorization and RBF network. Extensive experimental results on ORL and YALE face database show that the suggested method possesses much stronger analysis capability than the comparative methods. Compared with PCA, non-negative matrix factorization, kernel PCA and independent component analysis, the proposed face recognition method with WKNMF and RBF achieves over 10 % improvement in recognition accuracy.

A Study on Various Techniques Involved in Gender Prediction System: A Comprehensive Review

“…This technique counts the gradients (changes) areas in the local parts of an image. HOG descriptor shows high performance in various signature verification, identification [42] or face recognition application [43,44]. HOG is developed from SIFT algorithm [45].…”