Facial feature detection using AdaBoost with shape constraints

Recently a fast and efficient face detection method has been devised which relies on the AdaBoost algorithm and a set of Haar Wavelet like features. A natural extension of this approach is to use the same technique to locate individual features within the face region. However, we find that there is insufficient local structure to reliably locate each feature in every image, and thus local models can give many false positive responses. We demonstrate that the performance of such feature detectors can be significantly improved by using global shape constraints. We describe an algorithm capable of accurately and reliably detecting facial features and present quantitative results on both high and low resolution image sets.