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Published in:
2016 | OriginalPaper | Chapter
Recognizing Compound Emotional Expression in Real-World Using Metric Learning Method
Authors : Zhiwen Liu, Shan Li, Weihong Deng
Published in: Biometric Recognition
Publisher: Springer International Publishing
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Abstract
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.