Phase-based binocular vergence control and depth reconstruction using active vision
CVGIP: Image Understanding
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Facial Feature Points with Gabor Wavelets and Shape Models
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Tracking and Learning Graphs and Pose on Image Sequences of Faces
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Robust Pose Invariant Facial Feature Detection and Tracking in Real-Time
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Tracking Facial Feature Points with Statistical Models and Gabor Wavelet
MICAI '06 Proceedings of the Fifth Mexican International Conference on Artificial Intelligence
An efficient 3d head pose inference from videos
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Individual feature-appearance for facial action recognition
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
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This work presents a technique for automatic personalized facial features localization and tracking. The approach uses a set of subgraphs corresponding to the face deformable parts which are attached to a main subgraph with nodes consisting of more stable features, some of these nodes represent the anchor points of the more deformable subgraphs. At the node level, accurate positions are obtained by employing a Gabor phase---based disparity estimation technique. We used a modified formulation in which we have introduced a conditional disparity estimation procedure and a confidence measure as a similarity function that includes a phase difference term. A collection of trained graphs that were captured from different face deformations, are employed to correct the subgraph nodes tracking errors. Experimental results show that the facial feature points can be tracked with sufficient precision by establishing an effective self---correcting mechanism.