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Face plays significant role in social communication. This is a window to human personality, emotions and thoughts. According to the psychological research, nonverbal part is the most informative channel in social communication. Verbal part contributes about 7% of the message, vocal-34% and facial expression about 55%. Due to that, the face is a subject of study in many areas of science such as psychology, behavioural science, medicine and finally computer science. We propose an algorithm for facial expression recognition, which can classify the given image into one of the seven basic facial expression categories (Happiness, Sadness, Fear, Surprise, Anger, Disgust and neutral). The goal of this project is to design and implement the facial expression recognition system. The 2DPCA algorithm can be easily implemented in any programming language on a digital computer. 2DPCA algorithm is found to be very accurate and more effective. Our program has been tested using JAFFE Database who contains images of 10 women with 213 different facial expressions. Each image has a 256*256 pixel resolution; available at http://www.kasrl.org/jaffe.html using 113 images randomly selected for training and 100 images for testing, without any overlapping; we obtain a recognition rate equal to 94.22%.

Item Type	Conference
Divisions	» Laboratory of Electrical Engineering,Electronic and Renewable Energy » Faculty of Science and Technology
ePrint ID	446
Date Deposited	2015-12-02
Further Information	Google Scholar
URI	https://univ-soukahras.dz/en/publication/article/446

@inproceedings{uniusa446,
    title={Enhanced Facial Expression Recognition using 2DPCA Principal component Analysis and Gabor Wavelets},
    author={Narima Zermi and Mohammed Saaidia},
    year={2015},
    booktitle={International Conference on Automation Control, Telecommunications and Signals (ICATS'2015)}
}