Name
Abstract | ||
|---|---|---|
The aim of an automatic video-based facial expression recognition system is to detect and classify human facial expressions from image sequence. An integrated automatic system often involves two components: 1) peak expression frame detection and 2) expression feature extraction. In comparison with the image-based expression recognition system, the video-based recognition system often performs online detection, which prefers low-dimensional feature representation for cost-effectiveness. Moreover, effective feature extraction is needed for classification. Many recent recognition systems often incorporate rich additional subjective information and thus become less efficient for real-time application. In our facial expression recognition system, first, we propose the double local binary pattern (DLBP) to detect the peak expression frame from the video. The proposed DLBP method has a much lower-dimensional size and can successfully reduce detection time. Besides, to handle the illumination variations in LBP, logarithm-laplace (LL) domain is further proposed to get a more robust facial feature for detection. Finally, the Taylor expansion theorem is employed in our system for the first time to extract facial expression feature. We propose the Taylor feature pattern (TFP) based on the LBP and Taylor expansion to obtain an effective facial feature from the Taylor feature map. Experimental results on the JAFFE and Cohn-Kanade data sets show that the proposed TFP method outperforms some state-of-the-art LBP-based feature extraction methods for facial expression feature extraction and can be suited for real-time applications. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ACCESS.2017.2737821 | IEEE ACCESS |
Keywords | Field | DocType |
Pattern recognition,facial expression recognition,video processing,image processing | Computer vision,Facial recognition system,Expression Feature,Three-dimensional face recognition,Feature detection (computer vision),Pattern recognition,Computer science,Feature (computer vision),Local binary patterns,Feature extraction,Feature (machine learning),Artificial intelligence | Journal |
Volume | ISSN | Citations |
5 | 2169-3536 | 4 |
PageRank | References | Authors |
0.39 | 22 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanyuan Ding | 1 | 303 | 15.04 |
| Qin Zhao | 2 | 25 | 6.84 |
| Baoqing Li | 3 | 114 | 20.13 |
| Xiaobing Yuan | 4 | 19 | 3.49 |