A neural-AdaBoost based facial expression recognition system

The study improves expression recognition rate and execution time.Average recognition rates in JAFFE and Yale databases are 96.83% and 92.22%, respectively.The execution time for processing 100i?100 pixel size is 14.5ms.Best recognitions are happy, surprise, and disgust and the poorest is neutral.The general results are very encouraging when compared with others. This study improves the recognition accuracy and execution time of facial expression recognition system. Various techniques were utilized to achieve this. The face detection component is implemented by the adoption of Viola-Jones descriptor. The detected face is down-sampled by Bessel transform to reduce the feature extraction space to improve processing time then. Gabor feature extraction techniques were employed to extract thousands of facial features which represent various facial deformation patterns. An AdaBoost-based hypothesis is formulated to select a few hundreds of the numerous extracted features to speed up classification. The selected features were fed into a well designed 3-layer neural network classifier that is trained by a back-propagation algorithm. The system is trained and tested with datasets from JAFFE and Yale facial expression databases. An average recognition rate of 96.83% and 92.22% are registered in JAFFE and Yale databases, respectively. The execution time for a 100i?100 pixel size is 14.5ms. The general results of the proposed techniques are very encouraging when compared with others.

• Publication year

  2014

• Venue

  Expert systems with applications

• Publication date

  2014-06-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1016/j.eswa.2013.11.041
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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