Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast and Accurate Face Detector Based on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Name-It: Naming and Detecting Faces in News Videos
IEEE MultiMedia
Extracting Semantic Video Objects
IEEE Computer Graphics and Applications
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Simultaneous alpha map generation and 2-D mesh tracking for multimedia applications
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Unsupervised video segmentation based on watersheds and temporal tracking
IEEE Transactions on Circuits and Systems for Video Technology
Facial analysis and synthesis scheme
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
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Accurate and reliable automatic segmentation of faces in video footages is often hard to succeed, leading instead to laborious and tedious interactive manual segmentation. This paper presents a segmentation method that uses a few controlled sets of the weights on HSV components. First, it is shown that HSV has advantages over RGB or YCbCr when segmenting a face in image in such that a binary pattern reflects as many features of the face as possible. Then, a face detection system is constructed, in which each time a significant scene change is detected segmentation is carried out for the beginning frame of a new scene using a few sets of the weights on HSV components, and resulting patterns are correlated with a typical face pattern. Computer experiments show that the successful detection rate is more than 95 out of 100 faces.