Partial least squares regression on Grassmannian manifold for emotion recognition

• Main Authors: LIU, M., WANG, R., HUANG, Zhiwu, SHAN, S., CHEN, X.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• Subjects: emotion recognition; emotiw 2013 challenge; grassmannian manifolds; partial least squares regression; Databases and Information Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/6399; https://ink.library.smu.edu.sg/context/sis_research/article/7402/viewcontent/Partial_Least_Squares_Regression_on_Grassmannian.pdf
• Institution: Singapore Management University

In this paper, we propose a method for video-based human emotion recognition. For each video clip, all frames are represented as an image set, which can be modeled as a linear subspace to be embedded in Grassmannian manifold. After feature extraction, Class-specific One-to-Rest Partial Least Squares (PLS) is learned on video and audio data respectively to distinguish each class from the other confusing ones. Finally, an optimal fusion of classifiers learned from both modalities (video and audio) is conducted at decision level. Our method is evaluated on the Emotion Recognition In The Wild Challenge (EmotiW 2013). The experimental results on both validation set and blind test set are presented for comparison. The final accuracy achieved on test set outperforms the baseline by 26%