Finding sparse features for face detection using genetic algorithms

Sagha, H ; Sharif University of Technology | 2008

268 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/ICCCYB.2008.4721401
  3. Publisher: 2008
  4. Abstract:
  5. Although Face detection is not a recent activity in the field of image processing, it is still an open area for research. The greatest step in this field is the work reported by Viola and the recent analogous one is proposed by Huang et al. Both of them use similar features and also similar training process. The former is just for detecting upright faces, but the latter can detect multi-view faces in still grayscale images using new features called 'sparse feature'. Finding these features is very time consuming and inefficient by proposed methods. Here, we propose a new approach for finding sparse features using a genetic algorithm system. This method requires less computational cost and gets more effective features in learning process for face detection that causes more accuracy. © 2008 IEEE
  6. Keywords:
  7. Control theory ; Cybernetics ; Image processing ; Computational costs ; Face detections ; Gray-scale images ; Learning process ; Multi views ; New approaches ; Training process ; Industrial research
  8. Source: ICCC 2008 - IEEE 6th International Conference on Computational Cybernetics, Stara Lesna, 27 November 2008 through 29 November 2008 ; 2008 , Pages 179-182 ; 9781424428755 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4721401