Chapters authored
Matrix Factorization on Complex Domain for Face Recognition
Matrix factorization on complex domain is a natural extension of nonnegative matrix factorization, but it is still a very new trend in face recognition. In this chapter, we present two complex matrix factorization-based models for face recognition, in which the objective functions are the real-valued functions of complex variables. Our first model aims to build a learned base, which is embedded within original space. The second model finds the base whose volume is maximized. Experimental results on datasets with and without outliers show that our proposed algorithms are more effective than competitive algorithms.
Part of the book: Visual Object Tracking with Deep Neural Networks