Abstract: A method (100) of tracking facial features in a video sequence is disclosed. The method receives (110) as input facial features for tracking in a first frame of the video sequence. The video sequence is also segmented (120) using spatiotemporal segmentation to provide a sequence of associated two-dimensional segments, with a first segment in the sequence of associated two-dimensional segments including the facial features for tracking. Candidate facial features are then identified (140) in at least a second two-dimensional segment in the sequence of associated segments. Method (100) then verifies (155) which of the candidate facial features correspond with the facial features for tracking. Method (100) also recovers lost facial features by using known geometric relations between facial features.

Abstract: A method (100) and apparatus (700) are disclosed for detecting and tracking human faces across a sequence of video frames. Spatiotemporal segmentation is used to segment (115) the sequence of video frames into 3D segments. 2D segments are then formed from the 3D segments, with each 2D segment being associated with one 3D segment. Features are extracted (140) from the 2D segments and grouped into groups of features. For each group of features, a probability that the group of features includes human facial features is calculated (145) based on the similarity of the geometry of the group of features with the geometry of a human face model. Each group of features is also matched with a group of features in a previous 2D segment and an accumulated probability that said group of features includes human facial features is calculated (150). Each 2D segment is classified (155) as a face segment or a non-face segment based on the accumulated probability.

Abstract: A method (100) of locating human faces, if present, in a cluttered scene captured on a digital image (105) is disclosed. The method (100) relies on a two step process, the first being the detection of segments with a high probability of being human skin in the color image (105), and to then determine a bounday box, or other boundary indication, to border each of those segments. The second step (140) is the analysis of features within each of those boundary boxes to determine which of the segments are likely to be a human face. As human skin is not highly textured, in order to detect segments with a high probability of being human skin, a binary texture map (121) is formed from the image (105), and segments having high texture are discarded.

Abstract: A method (100) of tracking facial features in a video sequence is disclosed. The method receives (110) as input facial features for tracking in a first frame of the video sequence. The video sequence is also segmented (120) using spatiotemporal segmentation to provide a sequence of associated two-dimensional segments, with a first segment in the sequence of associated two-dimensional segments including the facial features for tracking. Candidate facial features are then identified (140) in at least a second two-dimensional segment in the sequence of associated segments. Method (100) then verifies (155) which of the candidate facial features correspond with the facial features for tracking. Method (100) also recovers lost facial features by using known geometric relations between facial features.

Abstract: A method (100) and apparatus (700) are disclosed for detecting and tracking human faces across a sequence of video frames. Spatiotemporal segmentation is used to segment (115) the sequence of video frames into 3D segments. 2D segments are then formed from the 3D segments, with each 2D segment being associated with one 3D segment. Features are extracted (140) from the 2D segments and grouped into groups of features. For each group of features, a probability that the group of features includes human facial features is calculated (145) based on the similarity of the geometry of the group of features with the geometry of a human face model. Each group of features is also matched with a group of features in a previous 2D segment and an accumulated probability that said group of features includes human facial features is calculated (150). Each 2D segment is classified (155) as a face segment or a non-face segment based on the accumulated probability.

Abstract: A method (100) of locating human faces, if present, in a cluttered scene captured on a digital image (105) is disclosed. The method (100) relies on a two step process, the first being the detection of segments with a high probability of being human skin in the colour image (105), and to then determine a bounday box, or other boundary indication, to border each of those segments. The second step (140) is the analysis of features within each of those boundary boxes to determine which of the segments are likely to be a human face. As human skin is not highly textured, in order to detect segments with a high probability of being human skin, a binary texture map (121) is formed from the image (105), and segments having high texture are discarded.