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Content Provider | IEEE Xplore Digital Library |
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Author | Garcia, C. Delakis, M. |
Copyright Year | 2002 |
Description | Author affiliation: Dept. of Comput. Sci., Crete Univ., Greece (Garcia, C.; Delakis, M.) |
Abstract | In this paper, we present a connectionist approach for detecting and precisely localizing semi-frontal human faces in complex images, making no assumption about the content or the lighting conditions of the scene, or about the size or the appearance of the faces. We propose a convolutional neural network architecture designed to recognize strongly variable face patterns directly from pixel images with no preprocessing, by automatically synthesizing its own set of feature extractors from a large training set of faces. We present in details the optimized design of our architecture, our learning strategy and the resulting process of face detection. We also provide experimental results to demonstrate the robustness of our approach and its capability to precisely detect extremely variable faces in uncontrolled environments. |
Starting Page | 44 |
Ending Page | 47 |
File Size | 367570 |
Page Count | 4 |
File Format | |
ISBN | 076951695X |
ISSN | 10514651 |
DOI | 10.1109/ICPR.2002.1048232 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2002-08-11 |
Publisher Place | Canada |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Robustness Face detection Humans Layout Neural networks Face recognition Pattern recognition Image recognition Pixel Data preprocessing |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition |
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