ABSTRACT
Tianrui Liu,a,b Wan-Chi Siu,a Cigdem Turan,a Shun-Cheung Lai,a
Perhaps, face is one of the most useful biometrics for person recog-
nition. Conventionally, three steps are required for face recognition,
namely face detection, face alignment and face recognition. In this
chapter, we just briefly mention the first two steps and concentrate
on some core technologies for face recognition. We will start the
chapter with a thorough introduction to using Eigenface or Principal
Component Analysis (PCA) for face recognition, since it is a classic
approach, and many modern approaches take it as a reference for
development. Subsequently, we briefly describe Linear Discriminant
Analysis (LDA) and other modern approaches, including subspace
learning methods and deep learning neural networks for face
recognition. We expect that the beginning part of this chapter will
be useful for beginners, whilst the rest of the chapter could form a
good reference for early researchers in the area.
