Mouth feature based smile detection

作者：Zhang Yuanzhi; Zhou Li^*; Sun Tao

来源：International Journal of Digital Content Technology and Its Applications, 2012, 6(23): 826-837.

DOI：10.4156/jdcta.vol6.issue23.96

• Detection time
• Face images
• Facial expression analysis
• Facial expression recognition
• Feature extraction algorithms
• Feature-based
• Image samples
• Intensity difference
• Memory consumption
• Mouth feature
• Research topics

Facial expression recognition is an interesting research topic. Considerable methods have been proposed in order to reach high accuracy in facial expression recognition, but only a few of these methods have considered factors like memory consumption and computational complexity. In this paper, we focus on smile detection which belongs to facial expression recognition. Compare with the proposed methods, we propose to use MF (Mouth Feature) as image samples instead of whole face images, which can significantly reduce the memory consumption. Intensity Difference is adopted as feature extraction algorithm. MFD (Maximum Feature Difference) algorithm is defined to reduce the large set of Intensity Difference features. Adaboost(adaptive boosting) is used to train a strong classifier. Experiments show that our approach can reach about 88% accuracy by examining 320 features, with a detection time of 9.3ms per face and a significant decrease of memory consumption by about 75%.

• 出版日期2012