Fast human face detection using successive face detectors with incremental detection capability

This paper concentrates on exploiting fast human face detection techniques for home video surveillance applications. The proposed method uses successive face detectors with incremental complexity and detection capability. The detectors are cascaded in such a way that each detector progressively restricts the possible face candidates into fewer areas. The proposed detectors, listed in the order of usage and complexity, are: (1) skin-color detector, (2) face structure detector which uses probability-based facial feature verification, and (3) three parallel learning-based detectors which take several representations of face candidates as inputs. The adopted representations are the pixel representation, the partial profile representation and the eigenface representation. The initial pruning of large areas of non-face regions significantly decreases the number of input windows for the learning-based face detector. This largely reduces the high computation cost for most learning-based detection approaches, while retaining the high detection accuracy and learning capabilities. Experimental results show that our proposal achieves an average of 0.3 - 0.4 second per frame processing speed with an image resolution of 320 by 240 pixels. An average of 92% detection rate is achieved for a test set composed of downloaded photos, standard test sequences and self-made sequences.