Abstract
In our daily life, the facial expression contains important information responded to interaction to other people. Human Facial Expression Recognition has been researched in the past years. Thus, this study adds facial muscle streak, for example nasal labial folds and front lines, as another recognition condition.
We used the traditional face detection to extract face area from original image. Then to extract eyes, mouth and eyebrow outlines’ position from face area. Afterward, we extracted important contours from different feature areas. Ultimately, we used these features to create a set of feature vector. Then, these vectors were used to process with neural network and to determine user’s facial expression.
In summary, this study used TFEID (Taiwanese Facial Expression Image Database) database to determine the expression recognition and face recognition. The experiment result shown, that 96.2% and 92.8% of TFEID database can be recognized in personalizing expression recognition experiment and full member expression recognition, respectively. In face recognition, 97.4% of TFEID sample were recognized.
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Lee, HC., Wu, CY., Lin, TM. (2013). Facial Expression Recognition Using Image Processing Techniques and Neural Networks. In: Pan, JS., Yang, CN., Lin, CC. (eds) Advances in Intelligent Systems and Applications - Volume 2. Smart Innovation, Systems and Technologies, vol 21. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35473-1_26
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DOI: https://doi.org/10.1007/978-3-642-35473-1_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35472-4
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