Title :
Learning a distribution-based face model for human face detection
Author :
Sung, Kah-Kay ; Poggio, Tomaso
Author_Institution :
Artificial Intelligence Lab., MIT, Cambridge, MA, USA
fDate :
31 Aug-2 Sep 1995
Abstract :
We present a distribution-based modeling cum example-based learning approach for detecting human faces in cluttered scenes. The distribution-based model captures complex variations in human face patterns that cannot be adequately described by classical pictorial template-based matching techniques or geometric model-based pattern recognition schemes. We also show how explicitly modeling the distribution of certain “facelike” nonface patterns can help improve classification results
Keywords :
biometrics (access control); face recognition; image recognition; learning by example; statistical analysis; cluttered scenes; complex variations; distribution-based face model learning; example-based learning; facelike nonface patterns; human face detection; human face patterns; Biological system modeling; Context modeling; Distance measurement; Face detection; Humans; Layout; Learning; Pattern matching; Pixel; Solid modeling;
Conference_Titel :
Neural Networks for Signal Processing [1995] V. Proceedings of the 1995 IEEE Workshop
Conference_Location :
Cambridge, MA
Print_ISBN :
0-7803-2739-X
DOI :
10.1109/NNSP.1995.514914
