Generic Face Alignment using Boosted Appearance Model

This paper proposes a discriminative framework for efficiently aligning images. Although conventional Active Appearance Models (AAM)-based approaches have achieved some success, they suffer from the generalization problem, i.e., how to align any image with a generic model. We treat the iterative image alignment problem as a process of maximizing the score of a trained two-class classifier that is able to distinguish correct alignment (positive class) from incorrect alignment (negative class). During the modeling stage, given a set of images with ground truth landmarks, we train a conventional Point Distribution Model (PDM) and a boosting-based classifier, which we call Boosted Appearance Model (BAM). When tested on an image with the initial landmark locations, the proposed algorithm iteratively updates the shape parameters of the PDM via the gradient ascent method such that the classification score of the warped image is maximized. The proposed framework is applied to the face alignme...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Xiaoming Liu 0002
• Keywords: Computer Vision, Conventional Active Appearance, CVPR 2007, Face Alignment Problem, Image Alignment Problem, Incorrect Alignment, Trained Two-class Classifier