Facial expression recognition by applying multi-step integral projection and SVMs

• Published in: 2009 IEEE Instrumentation and Measurement Technology Conference pp. 686 - 691
• Main Authors: Yisu Zhao, Xiaojun Shen, Georganas, Nicolas D
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2009
• Subjects: Computer vision; Data mining; Face detection; Face recognition; facial feature detection and extraction; Facial features; Gabor transformation; Humans; Lighting; multi-step integral projection; Robustness; Support Vector Machine; Support vector machine classification; Support vector machines
• ISBN: 9781424433520; 1424433525
• ISSN: 1091-5281
• DOI: 10.1109/IMTC.2009.5168537

In order to achieve subject-independent facial feature detection and extraction and obtain robustness against illumination variety, a novel method of facial expression recognition using the combination of multi-step integral projection and Gabor transformation for feature detection and SVM for classification is presented in this paper. First, to avoid manually picked expression features, we propose a new approach called multi-step integral projection to detect and locate the exact position of human facial features automatically. Second, we segment the extracted areas into small cells for 7×7 pixels each and apply Gabor transformation on each cell. This greatly reduces the execution time of the Gabor transformation while retaining important information. Third, a Support Vector Machine is used for classifying facial emotions and we tested our system on the JAFFE database while achieving a high recognition rate of 94.8357% on trained data. Finally, we discuss the effect of different parameters selection in Gabor transformation and analyze the reason for some incorrect recognition.