We present an efficient and robust model matching method which uses a joint shape and texture appearance model to generate a set of region template detectors. The model is fitted to an unseen image in an iterative manner by generating templates using the joint model and the current parameter estimates, correlating the templates with the target image to generate response images and optimising the shape parameters so as to maximise the sum of responses. The appearance model is similar to that used in the AAM [1]. However in our approach the appearance model is used to generate likely feature templates, instead of trying to approximate the image pixels directly. We show that when applied to human faces, our Constrained Local Model (CLM) algorithm is more robust and more accurate than the original AAM search method, which relies on the image reconstruction error to update the model parameters. We demonstrate improved localisation accuracy on two publicly available face data sets and improved tracking on a challenging set of in-car face sequences.

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We present an efficient method of fitting a set of local feature models to an image within the popular Active Shape Model (ASM) framework [3]. We compare two different types of non-linear boosted feature models trained using GentleBoost [9]. The first type is a conventional feature detector classifier, which learns a discrimination function between the appearance of a feature and the local neighbourhood. The second local model type is a boosted regression predictor which learns the relationship between the local neighbourhood appearance and the displacement from the true feature location. At run-time the second regression model is much more efficient as only the current feature patch needs to be processed. We show that within the local iterative search of the ASM the local feature regression provides improved localisation on two publicly available human face test sets as well as increasing the search speed by a factor of eight.

We describe a novel shape constraint technique which is incorporated into a multi-stage algorithm to automatically locate features on the human face. The method is coarse-to-fine. First a face detector is applied to find the approximate scale and location of the face in the image. Then individual feature detectors are applied and combined using a novel algorithm known as Pairwise Reinforcement of Feature Responses (PRFR). The points predicted by this method are then refined using a version of the Active Appearance Model (AAM) search, which is tuned to edge and corner features. The final output of the three stage algorithm is shown to give much better results than any other combination of methods. The method outperforms previous published results on the BIOID test set [11].

Recently a fast and efficient face detection method has been devised [11], 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.