ABSTRACT
Affective computing researchers have recently been focusing on continuous emotion dimensions like arousal and valence. This dual coordinate affect space can explain many of the discrete emotions like sadness, anger, joy, etc. In the area of continuous emotion recognition, Principal Component Analysis (PCA) models are generally used to enhance the performance of various image and audio features by projecting them to a new space where the new features are less correlated. We instead, propose that quantizing and projecting the features to a latent topic space performs better than PCA. Specifically we extract these topic features using Latent Dirichlet Allocation (LDA) models. We show that topic models project the original features to a latent feature space that is more coherent and useful for continuous emotion recognition than PCA. Unlike PCA where no semantics can be attributed to the new features, topic features can have a visual and semantic interpretation which can be used in personalized HCI applications and Assistive technologies. Our hypothesis in this work has been validated using the AVEC 2012 continuous emotion challenge dataset.
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. M. L. R., Mar. 2003. Google ScholarDigital Library
- P. Lade, V. Balasubramanian, H. Venkateswara, and S. Panchanathan. Detection of changes in human affect dimensions using an adaptive temporal topic model. In IEEE ICME, 2013.Google ScholarCross Ref
- P. Lucey, J. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews. The extended cohn-kanade dataset (ckGoogle Scholar
- ): A complete dataset for action unit and emotion-specified expression. In CVPRW, 2010.Google Scholar
- J. Nicolle, V. Rapp, K. Bailly, L. Prevost, and M. Chetouani. Robust continuous prediction of human emotions using multiscale dynamic cues. In ACM ICMI, 2012. Google ScholarDigital Library
- B. Schuller, M. Valstar, R. Cowie, and M. Pantic. Avec 2012: The continuous audio/visual emotion challenge - an introduction. In ACM ICMI, 2012. Google ScholarDigital Library
- M. Shah, L. Miao, C. Chakrabarti, and A. Spanias. A speech emotion recognition framework based on latent dirichlet allocation: Algorithm and fpga implementation. In ICASSP, 2013.Google ScholarCross Ref
- L. Shang and K.-P. Chan. A temporal latent topic model for facial expression recognition. In ACCV, 2010. Google ScholarDigital Library
- Y.-s. Shin. Recognizing facial expressions with pca and ica onto dimension of the emotion. pages 916--922. Springer Berlin Heidelberg, 2006. Google ScholarDigital Library
- Xuehan-Xiong and F. De la Torre. Supervised descent method and its application to face alignment. In IEEE CVPR, 2013.Google Scholar
Index Terms
Semantic feature projection for continuous emotion analysis
Recommendations
Emotion feature optimisation based on PCA-GRA analysis
The interference and redundancy of speech emotional features will directly affect the recognition performance of emotional features. In order to enhance the ability of emotional features to recognise speech emotion, dimension reduction method was used to ...
Identifying Sentence-Level Semantic Content Units with Topic Models
DEXA '10: Proceedings of the 2010 Workshops on Database and Expert Systems ApplicationsStatistical approaches to document content modeling typically focus either on broad topics or on discourse-level subtopics of a text. We present an analysis of the performance of probabilistic topic models on the task of learning sentence-level topics ...
Comments