A Novel Feature Extraction Technique for Facial Expression Recognition
This paper presents a new technique to extract the light invariant local feature for facial expression recognition. It is not only robust to monotonic gray-scale changes caused by light variations but also very simple to perform which makes it possible for analyzing images in challenging real-time settings. The local feature for a pixel is computed by finding the direction of the neighboring of the pixel with the particular rank in term of its gray scale value among all the neighboring pixels. When eight neighboring pixels are considered, the direction of the neighboring pixel with the second minima of the gray scale
intensity can yield the best performance for the facial expression
recognition in our experiment. The facial expression
classification in the experiment was performed using a support
vector machine on CK+ dataset The average recognition rate
achieved is 90.1 3.8%, which is better than other previous local feature based methods for facial expression analysis. The experimental results do show that the proposed feature extraction technique is fast, accurate and efficient for facial expression recognition.
Keywords: Emotion Recognition, Facial Expression Recognition, Image Processing, Local Descriptor, Pattern Recognition.
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ABOUT THE AUTHORS
Mohammad Shahidul Islam
Mohammad Shahidul Islam received his B.Tech. degree in Computer Science and Technology from Indian Institute of Technology-Roorkee (I.I.T-R), Uttar Pradesh, INDIA in 2002, M.Sc. degree in Computer Science from American World University, London Campus, U.K in 2005 and M.Sc. in Mobile Computing and Communication from University of Greenwich, London, U.K in 2008. He is currently pursuing the Ph.D. degree in Computer Science & Information Systems at National Institute of Development Administration (NIDA), Bangkok, THAILAND. His field of research interest includes Image Processing, Pattern Recognition, wireless and mobile communication, Satellite Commutation and Computer Networking.
Surapong Auwatanamongkol
Surapong Auwatanamong¬kol received a B.Eng. (Electrical Engineering) from Chulalongkor¬n University, THAILAND, in 1978 and M.S.(Computer Science) from Georgia Institute of Technol-ogy, U.S.A. in 1982 and Ph.D.(Computer Science) from Southern Methodist University, U.S.A. in 1991. Currently, he is an Associate Professor in Computer Science at the School of Applied Statistics, National Institute of Development Administration (NIDA), Thailand. His research interests include Evolutionary Computation, Pattern Recognition, Image processing and Data Mining.
Mohammad Shahidul Islam
Mohammad Shahidul Islam received his B.Tech. degree in Computer Science and Technology from Indian Institute of Technology-Roorkee (I.I.T-R), Uttar Pradesh, INDIA in 2002, M.Sc. degree in Computer Science from American World University, London Campus, U.K in 2005 and M.Sc. in Mobile Computing and Communication from University of Greenwich, London, U.K in 2008. He is currently pursuing the Ph.D. degree in Computer Science & Information Systems at National Institute of Development Administration (NIDA), Bangkok, THAILAND. His field of research interest includes Image Processing, Pattern Recognition, wireless and mobile communication, Satellite Commutation and Computer Networking.
Surapong Auwatanamongkol
Surapong Auwatanamong¬kol received a B.Eng. (Electrical Engineering) from Chulalongkor¬n University, THAILAND, in 1978 and M.S.(Computer Science) from Georgia Institute of Technol-ogy, U.S.A. in 1982 and Ph.D.(Computer Science) from Southern Methodist University, U.S.A. in 1991. Currently, he is an Associate Professor in Computer Science at the School of Applied Statistics, National Institute of Development Administration (NIDA), Thailand. His research interests include Evolutionary Computation, Pattern Recognition, Image processing and Data Mining.
