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A discriminated correlation classifier for face recognition

Published: 22 March 2010 Publication History

Abstract

In this paper, a discriminated correlation classifier is proposed to improve the performance of the two-dimensional (2-D) face recognition algorithm. Until now, many methods have been proposed to address the problems encountered by face recognition system, such as small number problem, pose and illumination variation, etc. All these works are aiming at enhancing the performance of the face recognition system. However, as far as we know, few work are concerning about how to improve the classifier, whose performance directly determines the final recognition accuracy. So, in this paper, our motivation is to improve the performance of correlation classifier, which is widely used in face recognition problems, to improve the recognition accuracy. Inspired by a correlation filter design method called Minimal Average Correlation Energy(MACE) filter, we propose a novel classifier called Discriminated Semi-Normalized Correlation (DSNC) classifier using our discriminative learning method. Compared with the classical discriminative learning methods that need many intra-class samples and can only be applied on close set recognition problems, our method needs only one intra-class sample and can be performed on open set face recognition problem. The validity of our method is tested on two benchmark face database(FRGC2.0 and FERET), and a private face database(THFaceID).

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Cited By

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  • (2016)MFAST Processing Model for Occlusion and Illumination Invariant Facial RecognitionAdvanced Computing and Communication Technologies10.1007/978-981-10-1023-1_16(161-170)Online publication date: 10-Jun-2016
  • (2010)An improved algorithm for face recognition using wavelet and facial parametersProceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia10.1145/1963564.1963573(51-58)Online publication date: 27-Dec-2010

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cover image ACM Conferences
SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing
March 2010
2712 pages
ISBN:9781605586397
DOI:10.1145/1774088
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 22 March 2010

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Author Tags

  1. FRGC2.0
  2. correlation classifier
  3. discriminative learning
  4. face recognition

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SAC'10: The 2010 ACM Symposium on Applied Computing
March 22 - 26, 2010
Sierre, Switzerland

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SAC '10 Paper Acceptance Rate 364 of 1,353 submissions, 27%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2016)MFAST Processing Model for Occlusion and Illumination Invariant Facial RecognitionAdvanced Computing and Communication Technologies10.1007/978-981-10-1023-1_16(161-170)Online publication date: 10-Jun-2016
  • (2010)An improved algorithm for face recognition using wavelet and facial parametersProceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia10.1145/1963564.1963573(51-58)Online publication date: 27-Dec-2010

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