Automatic Fiducial Points Detection for Facial Expressions Using Scale Invariant Feature

Defecting fiducial points successfully in facial images or video sequences call play an important role in numerous facial image interpretation tasks such as face detection and identification. facial expression recognition, emotion recognition. and face image database management. In this paper we propose all automatic and robust method of facial fiducial point's detection for facial expressions analysis in video sequences using scale invariant feature based Adaboost classifiers. Face region is first located using the face detector with local normalization and optimal adaptive correlation technique. Candidate points are their selected over the face region using local scale-space extrema detection. The scale invariant feature for each candidate point is extracted for further examination. We choose 26 fiducial points on the face region from training samples to build the fiducial point detectors with Adaboost classifiers. All the candidate points in file test samples are examined through these detectors. Finally, all the 26 facial fiducial points are located oil each frame of [lie test samples. Cohn-Kanade database and Mind Reading DVD are used for experiment. The results show that our method achieves a good performance of 90.69% average recognition rate.