Vector Array based Multi-view Face Detection with Compound Exemplars

We address the problem of Multiple View Face Detection (MVFD) in unconstrained environments. In order to achieve generalized face detection we use part-based image representations by tessellation of small image patches, which are typified by 2D vector arrays. Faces are detected by a method named Vector Array Recognition by Indexing and Sequencing (VARIS). VARIS is designed to find the optimal similarity matching between the input image and stored exemplars while allowing wide geometrical variations that are limited only by topological constraints. Aggregated similarity is further enhanced by matching the input images with compound exemplars. The novel compounding procedure also reduces the number of exemplars necessary for each class representation. VARIS with compounding performs efficient parallel classification and has polynomial computational complexity.