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This paper presents a new method for detecting faces in a video sequence where detection is not limited to frontal views. The three novel contributions of the paper are : (1) Accumulation of probabilities of detection over a sequence. This allows to obtain a coherent detection over time as well as independence from thresholds. (2) Prediction of the detection parameters which are position, scale and pose. This guarantees the accuracy of accumulation as well as a continuous detection. (3) The way pose is represented. The representation is based on the combination of two detectors, one for frontal views and one for profiles. Face detection is fully automatic and is based on the method developed by Schneiderman 13. It uses local histograms of wavelet coefficients represented with respect to a coordinate frame fixed to the object. A probability of detection is obtained for each image position, several scales and the two detectors. The probabilities of detection are propagated over time using a Condensation filter and factored sampling. Prediction is based on a zero order model for position, scale and "pose"; update uses the probability maps produced by the detection routine. Experiments show a clear improvement over frame-based detection results.
Mikolajczyk et al. (Thu,) studied this question.