This paper presents a fully automatic real-time face recognition system from video by using Active Appearance Models
(AAM) for fitting and tracking facial fiducial landmarks and warping the non-frontal faces into a frontal pose. By
implementing a face detector for locating suitable initialization step of the AAM shape searching and fitting process,
new facial images are interpreted and tracked accurately in real time (15fps). Using an Active Appearance Model
(AAM) for normalizing facial images under different poses and expressions is crucial to providing improved face
recognition performance as most systems degrade matching performance with even smallest pose variation.
Furthermore the AAM is a more robust feature registration tracking approach as most systems detect and locate the eyes
while AAMs detect and track multiple fiducial points on the face holistically. We show examples of AAM fitting and
tracking and pose normalization including an illumination pre-processing step to remove specular and cast shadow
illumination artifacts on the face. We show example pose normalization images as well as example matching scores
showing the improved performance of this pose correction method.
The Face Recognition Grand Challenge (FRGC) dataset is one of the most challenging datasets in the face recognition community, in this dataset we focus on the hardest experiment under the harsh un-controlled conditions. In this paper we compare how other popular face recognition algorithms like Direct Linear Discriminant Analysis (D-LDA) and Gram-Schmidt LDA methods compare to traditional eigenfaces, and fisherfaces. However, we also show that all these linear subspace methods can not discriminate faces well due to large nonlinear distortions in the face images. Thus we present our proposed Class dependence Feature Analysis (CFA) method which we demonstrate to produce superior performance compared to other methods by representing nonlinear features well. We perform this by extending the traditional CFA framework to use Kernel Methods and propose a proper choice of kernel parameters which improves the overall face recognition performance is significantly over the competing face recognition algorithms. We present results of this proposed approach on a large scale database from the Face Recognition Grand Challenge (FRGC)v2 which contains over 36,000 images focusing on Experiment 4 which poses the harshest scenario containing images captured under un-controlled indoor and outdoor conditions yielding significant illumination variations.
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