Name
Title | ||
|---|---|---|
Automatic affective dimension recognition from naturalistic facial expressions based on wavelet filtering and PLS regression |
Abstract | ||
|---|---|---|
Automatic affective dimension recognition from facial expression continuously in naturalistic contexts is a very challenging research topic but very important in human-computer interaction. In this paper, an automatic recognition system was proposed to predict the affective dimensions such as Arousal, Valence and Dominance continuously in naturalistic facial expression videos. Firstly, visual and vocal features are extracted from image frames and audio segments in facial expression videos. Secondly, a wavelet transform based digital filtering method is applied to remove the irrelevant noise information in the feature space. Thirdly, Partial Least Squares regression is used to predict the affective dimensions from both video and audio modalities. Finally, two modalities are combined to boost overall performance in the decision fusion process. The proposed method is tested in the fourth international Audio/Visual Emotion Recognition Challenge (AVEC2014) dataset and compared to other state-of-the-art methods in the affect recognition sub-challenge with a good performance. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/FG.2015.7284859 | 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) |
Keywords | Field | DocType |
AVEC2014 dataset,fourth international audio-visual emotion recognition challenge,decision fusion process,partial least square regression,irrelevant noise removal,wavelet transform based digital filtering method,audio segmentation,image frame,visual feature extraction,vocal feature extraction,naturalistic facial expression video,human-computer interaction,PLS regression,automatic affective dimension recognition | Mel-frequency cepstrum,Feature vector,Pattern recognition,Computer science,Partial least squares regression,Speech recognition,Feature extraction,Facial expression,Feature (machine learning),Artificial intelligence,Affect (psychology),Wavelet transform | Conference |
Volume | Citations | PageRank |
05 | 0 | 0.34 |
References | Authors | |
24 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yona Falinie A. Gaus | 1 | 40 | 3.87 |
| Hongying Meng | 2 | 832 | 69.39 |
| Asim Jan | 3 | 51 | 3.43 |
| Fan Zhang | 4 | 7 | 0.84 |
| Saeed Turabzadeh | 5 | 23 | 1.20 |