Coarse-to-fine support vector classifiers for face detection

H. Sahbi, N. Boujemaa
Object recognition supported by user interaction for service robots  
We describe a new hierarchical face detection algorithm which allows fast background rejection in major parts of images and fine processing in area containing faces. This coarse-to-fine classification strategy is based on learning support vector classifiers (SVMs) with increasing evaluation complexity (resp. decreasing invariance and false alarm rates) top-down in the hierarchy. The complexity, in terms of the number of support vectors, of each detector in the hierarchy is reduced by
more » ... duced by clustering. We introduce the bias variation technique which allows each simplified SVM function to satisfy the conservation hypothesis as a criterion to get a consistent classifier in terms of detection rate, false alarms and background rejection efficiency. Face detection is performed using a depth-first search and cancel strategy which, for a given "face pattern", finds a root-leaf path with a sequence of positive answers.
doi:10.1109/icpr.2002.1047868 dblp:conf/icpr/SahbiB02 fatcat:hnuoki337re7tb7n6p4ubcpynm