4EyesFace-Realtime face detection, tracking, alignment and recognition Changbo Hu, Rogerio Feris and Matthew Turk
Overview Introduction Face Detection and Pose tracking Face Alignment Face Recognition Conclusions
Introduction DetectionPose trackingAlignmentRecognition
Introduction Why this is a difficult problem? Facial Expressions, Illumination Changes, Pose, etc. Object Develop a fully automatic system, suitable for real-time applications to locate and track human faces, then to align and recognize the face. Evaluate it on a large dataset.
Face Detection [Viola and Jones, 2001] Simple features, which can be computed very fast. A variant of Adaboost is used both to select the features and to train the classifier. Classifiers are combined in a “cascade” which allows background regions of the image to be quickly discarded.
Face detection
Pose tracking Based on Kentaro Toyama ’ s IFA framework
Face Alignment Active Appearance Model (AAM) Statistical Shape Model (PCA) Statistical Texture Model (PCA)
Face alignment Problem: Partial Occlusion Active Wavelet Networks (AWN) (on BMVC ’ 03) Main idea: Replace AAM texture model by a wavelet network
Face Alignment Similar performance to AAM in images under normal conditions. More robust against partial occlusions.
Face Alignment Using 9 wavelets, the system requires only 3 ms per iteration. In general, at most 10 iterations are sufficiently for good convergence (PIV 1.6Ghz).
Multi-View Face Alignment View selection by pose tracker
Multi-View Face Alignment
Face recognition online recognition HMM based face recognition
Face recognition Large dataset evaluation FERET DataSet 1196 different individuals With ground truth of eye corners
Face recognition
Face Recognition
Conclusion We develop a system to do human face detection, tracking, alignment and recognition In this system, we invented new methods AWN and extent to multi- view AWN We implement the related detection and pose tracking Evaluate our method on large dataset