Active shape models—their training and application
Computer Vision and Image Understanding
A Landmark Paper in Face Recognition
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Facial Feature Detection and Tracking with Automatic Template Selection
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Multi-template ASM Method for Feature Points Detection of Facial Image with Diverse Expressions
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
A wavelet subspace method for real-time face tracking
Real-Time Imaging
Constraint shape model using edge constraint and Gabor wavelet based search
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
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We propose and evaluate methods for enhancing performances of lip contours localization and tracking, which are based on the concepts of Statistical Shape Models (e.g. Active Shape Models, Hybrid Active Shape Models) and optimization of multi features. A single feature-based ASM gets good performance only in particular conditions but gets stuck in local minimum or gives bad performance in noisy conditions. In this paper, we propose to use 3 features: Normal Profile, Grey Level Patches and Gabor Wavelets and combine them by using a voting approach to derive a robust method (MF-ASM) on lip contours detection. Since the original ASM does not take into account the temporal information from previous frames, the lip contours are tracked by replacing the standard ASM with our hybrid ASM which is capable to take advantage of temporal information. Initial experimental results using popular audio-visual database show that our methods are more robust to the local minimum problem and give higher accuracy than traditional single feature-based ASM in lip contours detection and tracking.