The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Convolutional face finder: a neural architecture for fast and robust face detection
Abstract
In this paper, we present a novel face detection approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns, rotated up to +/-20 degrees in image plane and turned up to +/-60 degrees, in complex real world images. The proposed system automatically synthesizes simple problem-specific feature extractors from a training set of face and nonface patterns, without making any assumptions or using any hand-made design concerning the features to extract or the areas of the face pattern to analyze. The face detection procedure acts like a pipeline of simple convolution and subsampling modules that treat the raw input image as a whole. We therefore show that an efficient face detection system does not require any costly local preprocessing before classification of image areas. The proposed scheme provides very high detection rate with a particularly low level of false positives, demonstrated on difficult test sets, without requiring the use of multiple networks for handling difficult cases. We present extensive experimental results illustrating the efficiency of the proposed approach on difficult test sets and including an in-depth sensitivity analysis with respect to the degrees of variability of the face patterns.
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Cites
Gradient-based learning applied to document recognition
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Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
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Neural network-based face detection
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Original approach for the localisation of objects in images
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Cited by
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
A survey of the recent architectures of deep convolutional neural networks
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09825-6 · Cited by 3,137
Learning Hierarchical Features for Scene Labeling
Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.231 · Cited by 2,620
Convolutional networks and applications in vision
LeCun, Y., Kavukcuoglu, K., & Farabet, C. (2010) · Proceedings of 2010 IEEE International Symposium on Circuits and Systems · doi:10.1109/iscas.2010.5537907 · Cited by 2,213
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The 2005 PASCAL Visual Object Classes Challenge
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Self-citationTu, Z. (2005) · Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 · doi:10.1109/iccv.2005.194 · Cited by 397
CNP: An FPGA-based processor for Convolutional Networks
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Metadata
- Authors
- Garcia, C., & Delakis, M.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2004-11
- Volume
- 26
- Issue
- 11
- Pages
- 1408-1423
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2120284346
- Cited by
- 547
- References
- 4
- FWCI
- 28.87
- Type
- journal article
- Language
- en
- Open access
- closed
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