Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its newest applications.
Face recognition (FR) has become a popular research topic in the computer vision,
image processing, and pattern recognition areas. Recognition performance of the
practical FR system is largely influenced by the variations in illumination conditions,
viewing directions or poses, facial expression, aging, and disguises. FR
provides the wide applications in commercial, law enforcement, and military, and
so on, such as airport security and access control, building surveillance and
monitoring, human–computer intelligent interaction and perceptual interfaces,
smart environments at home, office, and cars. An excellent FR method should
consider what features are used to represent a face image and how to classify a
new face image based on this representation. Current feature extraction methods
can be classified into signal processing and statistical learning methods. On signalprocessing-
based methods, feature-extraction-based Gabor wavelets are widely
used to represent the face image, because the kernels of Gabor wavelets are similar
to two-dimensional receptive field profiles of the mammalian cortical simple cells,
which capture the properties of spatial localization, orientation selectivity, and
spatial frequency selectivity to cope with the variations in illumination and facial
expressions. On the statistical-learning-based methods, the dimension reduction
methods are widely used. In this book, we have more attentions on learning-based
FR method. In the past research, the current methods include supervised learning,
unsupervised learning, and semi-supervised learning.
