Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multidimensional binary search trees used for associative searching
Communications of the ACM
A Cooperative Algorithm for Stereo Matching and Occlusion Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images under Variable Illumination
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Comparative Study of Coarse Head Pose Estimation
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Handbook of Face Recognition
A Stereo and Color-based Method for Face Pose Estimation and Facial Feature Extraction
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Robust real-time 3D head pose estimation from range data
Pattern Recognition
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This paper describes a novel method for analyzing single faces of non-cooperative persons on the basis of stereoscopic color images. The challenges arise from the fact that the persons observed are non-cooperative, which in turn complicates further processing as facial feature extraction and tracking in image sequence. In our method, face detection is based on color-driven clustering of 3D points derived from stereo. A mesh model is registered with a post-processed face cluster, using a variant of the Iterative Closest Point algorithm (ICP). The pose is derived from correspondence. Then, the pose and model information are used for face normalization and facial feature localization. Automatic extraction of facial features is carried out using modified Active Shape Models (ASM). In contrast to the simple ASM, another approach is pursued in this work. It involves two modifications to the ASM, which lead to greater stability and robustness. The results show that stereo and color are powerful cues for finding the face and its pose, and for facial feature extraction under a wide range of poses, illumination types and expressions (PIE).