Context-based additive logistic model for facial keypoint localization

Abstract

Facial keypoint localization is an important step for face recognition. The "Average of Synthetic Exact Filter (ASEF)" approach [2] finds a correlation filter for each training image and averages them together. The resulting classifier is efficient as the filtering can be implemented in the Fourier domain and performance is good for frontal images. However, it cannot cope with a range of poses. In this paper, we generalize this approach to find keypoints using a technique that (i) combines together information from training images in a more principled way than averaging, (ii) can be extended to form non-linear combinations of filters and (iii) can adapt based on context (e.g. pose). These innovations are presented within a greedy boosting-style probabilistic framework. We demonstrate state of the art performance of these algorithms using a challenging data set. © 2010. The copyright of this document resides with its authors.