Deep learning vs. kernel methods: Performance for emotion prediction in videos

• Published in: International Conference on Affective Computing and Intelligent Interaction and workshops pp. 77 - 83
• Main Authors: Baveye, Yoann, Dellandrea, Emmanuel, Chamaret, Christel, Liming Chen
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.09.2015
• Subjects: affective computing; benchmarking; Computation; Computational modeling; Conferences; continuous emotion prediction; Correlation; deep learning; Emotions; Hidden Markov models; Kernel; Learning; Licenses; Machine learning; Mathematical models; Motion pictures; Neural networks; Regression; Videos
• ISSN: 2156-8111
• DOI: 10.1109/ACII.2015.7344554

Recently, mainly due to the advances of deep learning, the performances in scene and object recognition have been progressing intensively. On the other hand, more subjective recognition tasks, such as emotion prediction, stagnate at moderate levels. In such context, is it possible to make affective computational models benefit from the breakthroughs in deep learning? This paper proposes to introduce the strength of deep learning in the context of emotion prediction in videos. The two main contributions are as follow: (i) a new dataset, composed of 30 movies under Creative Commons licenses, continuously annotated along the induced valence and arousal axes (publicly available) is introduced, for which (ii) the performance of the Convolutional Neural Networks (CNN) through supervised fine-tuning, the Support Vector Machines for Regression (SVR) and the combination of both (Transfer Learning) are computed and discussed. To the best of our knowledge, it is the first approach in the literature using CNNs to predict dimensional affective scores from videos. The experimental results show that the limited size of the dataset prevents the learning or finetuning of CNN-based frameworks but that transfer learning is a promising solution to improve the performance of affective movie content analysis frameworks as long as very large datasets annotated along affective dimensions are not available.