Integrated Person Tracking Using Stereo, Color, and Pattern Detection
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Vision and Applications - Special issue: IEEE WACV
Model-Based Head Pose Tracking With Stereovision
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Real-Time Stereo Tracking of Multiple Moving Heads
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
Robust Real-Time Face Detection
International Journal of Computer Vision
Using image depth information for fast face detection
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Video-Rate hair tracking system using kinect
CCIW'13 Proceedings of the 4th international conference on Computational Color Imaging
A hardware architecture for real-time object detection using depth and edge information
ACM Transactions on Embedded Computing Systems (TECS)
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In the case that the sizes of faces are not available, all possible sizes of faces have to be assumed and a face detector has to classify many (often ten or more) sub-image regions everywhere in an image. This makes the face detection slow and the high false positive rate. This paper explores the usage of depth information for accelerating the face detection and reducing the false positive rate at the same time. In detail, we use the depth information to determine the size of the sub-image region that needs to be classified for each pixel. This will reduce the number of sub-image regions that need to be classified from many to one for one position (pixel) in an image. Since most unnecessary classifications are effectively avoided, both the processing time for face detection and the possibility of false positive can be reduced greatly. We also propose a fast algorithm for estimating the depth information that is used to determine the size of sub-image regions to be classified.