CMAC WITH CLUSTERING MEMORY AND ITS APPLICATION TO FACIAL EXPRESSION RECOGNITION

Please use this identifier to cite or link to this item: http://ir.lib.ncut.edu.tw/handle/987654321/6886

Title:	CMAC WITH CLUSTERING MEMORY AND ITS APPLICATION TO FACIAL EXPRESSION RECOGNITION
Authors:	林灶生
Contributors:	資訊工程系
Date:	2011-09
Issue Date:	2017-12-17 13:45:42 (UTC+8)
Abstract:	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 Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218001411008968
Relation:	International Journal of Pattern Recognition and Artificial Intelligence
Appears in Collections:	[Department of Computer Science and Information Engineering] 【資訊工程系所】期刊論文

Files in This Item:

There are no files associated with this item.

View Licence