Recognizing Compound Emotional Expression in Real-World Using Metric Learning Method
Understanding human facial expressions plays an important role in Human-Computer-Interaction (HCI). Recent achievements on automatically recognizing facial expressions are mostly based on lab-controlled databases, in which facial images are a far cry from those in the real world. The main contribution of this paper is listed in the following three points. First, a large real-world facial expression database (RAF-DB), with nearly 30,000 images collected from Flickr and labeled by 300 volunteers will be introduced. Second, for the reason that human emotions are much more complexed than the six-basic-emotion defined by Ekman et al., we re-categories real-world facial expressions as compound emotional expressions, which can explain human emotions better. Finally, a metric learning method as well as several state-of-the-art facial expression classifying methods including SVM, are used to recognize our compound expression dataset. And we found that metric learning method performed better than other classifications.
KeywordsCompound facial expressions Large Real-world Database Metric learning
This work was partially sponsored by supported by the NSFC (National Natural Science Foundation of China) under Grant No. 61375031, No. 61573068, No. 61471048, and No. 61273217, the Fundamental Research Funds for the Central Universities under Grant No. 2014ZD03-01, This work was also supported by Beijing Nova Program, CCF-Tencent Open Research Fund, and the Program for New Century Excellent Talents in University.
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