This paper presents our proposed approach for the second Emotion Recognition in The Wild Challenge. We propose a new feature descriptor called Histogram of Oriented Gradients from Three Orthogonal Planes (HOG_TOP) to represent facial expressions. We also explore the properties of visual features and audio features, and adopt Multiple Kernel Learning (MKL) to find an optimal feature fusion. An SVM with multiple kernels is trained for the facial expression classification. Experimental results demonstrate that our method achieves a promising performance. The overall classification accuracy on the validation set and test set are 40.21% and 45.21%, respectively. Copyright © 2014 ACM.
|Title of host publication||ICMI '14: Proceedings of the 16th International Conference on Multimodal Interaction|
|Place of Publication||New York|
|Publisher||Association for Computing Machinery|
|ISBN (Print)||9781450328852, 1450328857|
|Publication status||Published - Nov 2014|