An approach to facial expression analysis with multi-model interactions

作者：Xu Chao^*; Hu Qinghua; Xu Guangquan; Feng Zhiyong

来源：International Journal of Computer Mathematics, 2014, 91(11): 2329-2340.

DOI：10.1080/00207160.2013.802309

• 68U10
• 68T10
• adaptive principal component analysis
• neuron-computing
• multi-model interaction
• clustering
• facial expression analysis

Affective computing is important to the human-computer interaction, while facial expression analysis is the core issue. In this paper, a multi-model approach is proposed to achieve rational and satisfactory results for real-time facial expression analysis. With the advantages of multi-model interaction, the traditional process of cluster construction is optimized for the facial expression cluster structures according to the multi-model selection, distribution and evaluation interactions. Experiments are conducted to evaluate the rationality of the multi-model approach outlined in this paper. The satisfactory results demonstrate favourable performance comparable to the best results achieved through the cooperative neuron-computing interactions. Not only can the resultant approach construct the cluster distribution efficiently and accurately, but also it is capable of achieving high-quality and high-convergence interactive computing.

• 出版日期2014-11-2
• 单位天津大学