TY - JOUR
T1 - CMAC with clustering memory and its application to facial expression recognition
AU - Liao, Yu Yi
AU - Lin, Jzau Sheng
AU - Tai, Shen Chuan
N1 - Funding Information:
This work was partially supported by the National Science Council, Taiwan, R.O.C., under the Grant NSC96-2221-E-167-019-MY2. The authors also thank Dr. Michael J. Lyons for providing the facial expression database and more information in Ref. 39.
PY - 2011/11
Y1 - 2011/11
N2 - 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.
AB - 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.
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U2 - 10.1142/S0218001411008968
DO - 10.1142/S0218001411008968
M3 - Article
AN - SCOPUS:83755186208
SN - 0218-0014
VL - 25
SP - 1055
EP - 1072
JO - International Journal of Pattern Recognition and Artificial Intelligence
JF - International Journal of Pattern Recognition and Artificial Intelligence
IS - 7
ER -