Markovian Mixture Face Recognition with discriminative face alignment
Venue
automatic face and gesture recognition, ieee (2008)
Publication Year
2008
Authors
BibTeX
Abstract
A typical automatic face recognition system is composed of three parts: face
detection, face alignment and face recognition. Conventionally, these three parts
are processed in a bottom-up manner: face detection is performed first, then the
results are passed to face alignment, and finally to face recognition. The
bottom-up approach is one extreme of vision approaches. The other extreme approach
is top-down. In this paper, we proposed a stochastic mixture approach for combining
bottom-up and top-down face recognition: face recognition is performed from the
results of face alignment in a bottom-up way, and face alignment is performed based
on the results of face recognition in a top-down way. By modeling the mixture face
recognition as a stochastic process, the recognized person is decided
probabilistically according to the probability distribution coming from the
stochastic face recognition, and the recognition problem becomes that “who the most
probable person is when the stochastic process of face recognition goes on for a
long time or ideally for an infinite duration”. This problem is solved with the
theory of Markov chains by modeling the stochastic process of face recognition as a
Markov chain. As conventional face alignment is not suitable for this mixture
approach, discriminative face alignment is proposed. And we also prove that the
stochastic mixture face recognition results only depend on discriminative face
alignment, not on conventional face alignment. The effectiveness of our approach is
shown by extensive experiments.
