From Few to Many: Generative Models for Recognition Under Variable Pose and Illumination
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
View influence analysis and optimization for multiview face recognition
Journal on Image and Video Processing
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We present data collection and recognition experiment focused on multi-view face recognition/descriptor. Many face databases and face recognition systems have been constructed and experimented in terms of various illumination, time, poses, or expressions. However none of databases yet satisfies a large variation of poses to study systematic 3D human face information, which results unsatisfactory success rate for the posed face recognition while many quite satisfactory frontal view reconstructions have been shown. It is due to the difficulty of data collection of facial images to satisfy the large variation of poses to fully represent the 3D characteristic of human faces. We show two possible multi-view face data collection either using rendering of 3D models or using a video camera. We also illustrate our approach to build a face descriptor containing 3D information of human face using multiview concepts. This multi-view face recognition descriptor is a 3D face descriptor which takes systematic extension of 2D face descriptor using the concept how much powerful a view influences over nearby views, so called as "quasi-view" size.