Banks, retail stores, stadiums, airports and other facilities use facial recognition to reduce crime and prevent violence.
So in short, while all facial recognition systems use face detection, not all face detection systems have a facial recognition component.
This thesis concerns face recognition in uncontrolled environments in which the images used for training and test are collected from the real world instead of laboratories.
Compared with controlled environments, images from uncontrolled environments contain more variation in pose, lighting, expression, occlusion, background, image quality, scale, and makeup.
Therefore, face recognition in uncontrolled environments is much more challenging than in controlled conditions.
Moreover, many real world applications require good recognition performance in uncontrolled environments.
Second, most current algorithms cannot handle large pose variation, which has become a bottleneck for improving performance.
In this thesis, we investigate Bayesian models for face recognition.
We call this new algorithm multi-scale PLDA and our experiments show it can handle lighting variation better than PLDA but fails for pose variation.
We then analyze three existing Bayesian face recognition algorithms and combine the advantages of PLDA and the Joint Bayesian Face algorithm [Chen et al. We find that our new algorithm improves performance compared to existing Bayesian face recognition algorithms.