Facial feature point detection under large range of face deformations

Facial Feature Point Detection (FFPD) plays a significant role in several face analysis tasks such as feature extraction and classification. This paper presents a Fully Automatic FFPD system using the application of Random Forest Regression Voting in a Constrained Local Model (RFRV-CLM) framework. A global detector is used to find the approximate positions of the facial region and eye centers. A sequence of local RFRV-CLMs are used to locate a detailed set of points around the facial features. Both global and local models use Random Forest Regression to vote for optimal positions. The system is evaluated in the task of facial expression localization using five different facial expression databases of different characteristics including age, intensity, 6-basic expressions, 22 compound expressions, static and dynamic images, and deliberate and spontaneous expressions. Quantitative results of the evaluation of automatic point localization against manual points (ground truth) demonstrated that the results of the proposed approach are encouraging and outperform the results of alternative techniques tested on the same databases.