CMAC with clustering memory and its application to facial expression recognition

Yu Yi Liao, Jzau Sheng Lin, Shen Chuan Tai

Research output: Contribution to journalArticle

3 Citations (Scopus)


In this paper, a facial expression recognition system based on cerebella model articulation controller with a clustering memory (CMAC-CM) is presented. Firstly, the facial expression features were automatically preprocessed and extracted from given still images in the JAFFE database in which the frontal view of faces were contained. Next, a block of lower frequency DCT coefficients was obtained by subtracting a neutral image from a given expression image and rearranged as input vectors to be fed into the CMAC-CM that can rapidly obtain output using nonlinear mapping with a look-up table in training or recognizing phase. Finally, the experimental results have demonstrated recognition rates with various block sizes of coefficients in lower frequency and cluster sizes of weight memory. A mean recognition rate of 92.86% is achieved for the testing images. CMAC-CM takes 0.028 seconds for test image in testing phase.

Original languageEnglish
Pages (from-to)1055-1072
Number of pages18
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number7
Publication statusPublished - 2011 Nov 1

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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