A new incremental face recognition system

Aliyari Ghassabeh, Y ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/IDAACS.2007.4488435
  3. Publisher: 2007
  4. Abstract:
  5. In this paper, we present new adaptive linear discriminant analysis (LDA) algorithm and apply them for adaptive facial feature extraction. Adaptive nature of the proposed algorithm is advantageous for real world applications in which one confronts with a sequence of data such as online face recognition and mobile robotics. Application of the new algorithm on feature extraction from facial image sequences is given in three steps: i) adaptive image preprocessing, ii) adaptive dimension reduction and iii) adaptive LDA feature estimation. Steps 1 and 2 are done simultaneously and outputs of stage 2 are used as a sequence of inputs for stage3. The proposed system was tested on Yale and PIE face databases. Experimental results on these databases demonstrated the effectiveness of the proposed system for adaptive estimation of feature space for online face recognition. ©2007 IEEE
  6. Keywords:
  7. Adaptive algorithms ; Automation ; Computer systems ; Data acquisition ; Database systems ; Discriminant analysis ; Estimation ; Feature extraction ; Intelligent systems ; Mergers and acquisitions ; Technology ; Technology transfer ; Adaptive dimension reduction ; Adaptive estimations ; Adaptive linear discriminant analysis ; Computing systems ; Dimension reduction ; Face databases ; Face recognition system ; Facial feature extraction ; Facial imaging ; Feature estimation ; Feature spaces ; Image pre-processing ; Incremental face recognition system ; Intelligent data ; Linear discriminant analysis (LDA) ; Mobile robotics ; New algorithm ; Real world applications ; Face recognition
  8. Source: 2007 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS, Dortmund, 6 September 2007 through 8 September 2007 ; 2007 , Pages 335-340 ; 1424413486 (ISBN); 9781424413485 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4488435