CMAC with clustering memory and its application to facial expression recognition

Yu Yi Liao, Jzau Sheng Lin, Shen Chuan Tai

研究成果: Article

3 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)1055-1072
頁數18
期刊International Journal of Pattern Recognition and Artificial Intelligence
25
發行號7
DOIs
出版狀態Published - 2011 十一月 1

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

指紋 深入研究「CMAC with clustering memory and its application to facial expression recognition」主題。共同形成了獨特的指紋。

  • 引用此