Catálogo de publicaciones - libros

A key problem for “face in the crowd” recognition from existing surveillance cameras in public spaces (such as mass transit centres) is the issue of pose mismatches between probe and gallery faces. In addition to accuracy, scalability is also important, necessarily limiting the complexity of face classification algorithms. In this paper we evaluate recent approaches to the recognition of faces at relatively large pose angles from a gallery of frontal images and propose novel adaptations as well as modifications. Specifically, we compare and contrast the accuracy, robustness and speed of an Active Appearance Model (AAM) based method (where realistic frontal faces are synthesized from non-frontal probe faces) against bag-of-features methods (which are local feature approaches based on block Discrete Cosine Transforms and Gaussian Mixture Models). We show a novel approach where the AAM based technique is sped up by directly obtaining pose-robust features, allowing the omission of the computationally expensive and artefact producing image synthesis step. Additionally, we adapt a technique to face classification and contrast its properties to a previously proposed method. We also show that the two bag-of-features approaches can be considerably sped up, without a loss in classification accuracy, via an approximation of the exponential function. Experiments on the FERET and PIE databases suggest that the bag-of-features techniques generally attain better performance, with significantly lower computational loads. The technique is capable of achieving an average recognition accuracy of 89% for pose angles of around 25 degrees.

This paper describes new feed-forward architectural and configural/holistic strategies for robust face recognition. This includes adaptive and robust correlation filters that lock on both appearance and location, and recognition-by-parts using boosting over strangeness driven weak learners. The utility of the proposed architectural strategies, shown with respect to different databases, includes occlusion, disguise, and temporal changes. The results obtained confirm and complement key findings on the ways people recognize each other, among them that the facial features are processed holistically and that the eyebrows are among the most important features for recognition.