2014/08/16 by Sébastien Ouellet, Ouellet, Sébastien
Computer Science · Psychology · #Computer Vision and Pattern Recognition (cs.CV) #Emotion and Mood Recognition #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Social Robot Interaction and HRI #cs.CV #cs.LG #cs.NE
paper · pdf · doi:10.48550/arxiv.1408.3750
6 pages, 8 figures, IEEE style
arxiv created 2014/08/16 · openalex publication_date 2014/08/16 · arxiv updated 2014/08/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The goal of the present study is to explore the application of deep convolutional network features to emotion recognition. Results indicate that they perform similarly to other published models at a best recognition rate of 94.4%, and do so with a single still image rather than a video stream. An implementation of an affective feedback game is also described, where a classifier using these features tracks the facial expressions of a player in real-time.