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Genetic algorithm-optimised structure of convolutional neural network for face recognition applications

Rikhtegar, A ; Sharif University of Technology | 2016

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  1. Type of Document: Article
  2. DOI: 10.1049/iet-cvi.2015.0037
  3. Publisher: Institution of Engineering and Technology , 2016
  4. Abstract:
  5. Proposing a proper method for face recognition is still a challenging subject in biometric and computer vision applications. Although some reliable systems were introduced under relatively controlled conditions, their recognition rate is not satisfactory in the general settings. This is especially true when there are variations in pose, illumination, and facial expression. To alleviate these problems, a hybrid face recognition system is proposed which benefits from the superiority of both convolutional neural network (CNN) and support vector machine (SVM). To this end, first a genetic algorithm is employed to find the optimum structure of CNN. Then, the performance of the system is improved by replacing the last layer of CNN with an ensemble of SVMs. Finally, using concepts of error correction, decision is made. The potential of CNN as a trainable feature extractor provides a flexible recognition system that can recognise faces with variations in pose and illumination. Simulation results show that the system achieves good recognition rate and is robust against variations in terms of facial expressions, occlusion, noise, and illuminations
  6. Keywords:
  7. Algorithms ; Computer vision ; Convolution ; Error correction ; Genetic algorithms ; Neural networks ; Structural optimization ; Support vector machines ; Computer vision applications ; Controlled conditions ; Convolutional neural network ; Face recognition systems ; Facial expressions ; Feature extractor ; Optimum structures ; Recognition systems ; Face recognition
  8. Source: IET Computer Vision ; Volume 10, Issue 6 , 2016 , Pages 559-566 ; 17519632 (ISSN)
  9. URL: http://ieeexplore.ieee.org/document/7557121