Face Detection Using Multiple Cues

Many potential applications exist where a fast and robust detection of human faces is required. Different cues can be used for this purpose. Since each cue has its own pros and cons we, in this paper, suggest to combine several complimentary cues in order to gain more robustness in face detection. Concretely, we apply skin-color, shape, and texture to build a robust detector. We define the face detection problem in a state-space spanned by position, scale, and rotation. The statespace is searched using a Particle Filter where 80% of the particles are predicted from the past frame, 10% are chosen randomly and 10% are from a texture-based detector. The likelihood of each selected particle is evaluated using the skin-color and shape cues. We evaluate the different cues separately as well as in combination. An improvement in both detection rates and false positives is obtained when combining them.