IEEE TRANSACTIONS ON IMAGE PROCESSING 1 Feature-Based Lucas-Kanade and Active Appearance Models

among the most commonly used methods for image alignment and facial fitting, respectively. They both utilize non-linear gradient descent, which is usually applied on intensity values. In this paper, we propose the employment of highly-descriptive, densely-sampled image features for both problems. We show that the strategy of warping the multi-channel dense feature image at each iteration is more beneficial than extracting features after warping the intensity image at each iteration. Motivated by this observation, we demonstrate robust and accurate alignment and fitting performance using a variety of powerful feature descriptors. Especially with the employment of HOG and SIFT features, our method significantly outperforms the current state-of-the-art results on in-the-wild databases. Index Terms—Lucas-Kanade, Active Appearance Models, dense image feature descriptors, face alignment, face fittin