An approach to facial expression analysis with multi-model interactions

Abstract

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.

Keywords:

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

2010 AMS Subject Classifications:

• 68T10
• 68U10

This work is partially supported by the National 973 Foundation of China (No. 2013CB329301), the National 985 Foundation of China and National Science Foundation of China (No. 61222210).