TY - JOUR
T1 - Combining texture and stereo disparity cues for real-time face detection
AU - Jiang, Feijun
AU - Fischer, Mika
AU - Ekenel, Hazim Kemal
AU - Shi, Bertram E.
PY - 2013/10
Y1 - 2013/10
N2 - Intuitively, integrating information from multiple visual cues, such as texture, stereo disparity, and image motion, should improve performance on perceptual tasks, such as object detection. On the other hand, the additional effort required to extract and represent information from additional cues may increase computational complexity. In this work, we show that using biologically inspired integrated representation of texture and stereo disparity information for a multi-view facial detection task leads to not only improved detection performance, but also reduced computational complexity. Disparity information enables us to filter out 90% of image locations as being less likely to contain faces. Performance is improved because the filtering rejects 32% of the false detections made by a similar monocular detector at the same recall rate. Despite the additional computation required to compute disparity information, our binocular detector takes only 42 ms to process a pair of 640×480 images, 35% of the time required by the monocular detector. We also show that this integrated detector is computationally more efficient than a detector with similar performance where texture and stereo information is processed separately.
AB - Intuitively, integrating information from multiple visual cues, such as texture, stereo disparity, and image motion, should improve performance on perceptual tasks, such as object detection. On the other hand, the additional effort required to extract and represent information from additional cues may increase computational complexity. In this work, we show that using biologically inspired integrated representation of texture and stereo disparity information for a multi-view facial detection task leads to not only improved detection performance, but also reduced computational complexity. Disparity information enables us to filter out 90% of image locations as being less likely to contain faces. Performance is improved because the filtering rejects 32% of the false detections made by a similar monocular detector at the same recall rate. Despite the additional computation required to compute disparity information, our binocular detector takes only 42 ms to process a pair of 640×480 images, 35% of the time required by the monocular detector. We also show that this integrated detector is computationally more efficient than a detector with similar performance where texture and stereo information is processed separately.
KW - Disparity energy model
KW - Gabor filter
KW - Multi-view face detection
KW - Stereo vision
UR - http://www.scopus.com/inward/record.url?scp=84884594092&partnerID=8YFLogxK
U2 - 10.1016/j.image.2013.07.006
DO - 10.1016/j.image.2013.07.006
M3 - Article
AN - SCOPUS:84884594092
SN - 0923-5965
VL - 28
SP - 1100
EP - 1113
JO - Signal Processing: Image Communication
JF - Signal Processing: Image Communication
IS - 9
ER -