Adaptive Robust Local Complete Pattern For Facial Expression Recognition

Rubel, Al Shahriar; Chowdhury, Ahsan

dc.contributor.author	Rubel, Al Shahriar
dc.contributor.author	Chowdhury, Ahsan
dc.date.accessioned	2020-10-19T16:53:57Z
dc.date.available	2020-10-19T16:53:57Z
dc.date.issued	2018-11-15
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dc.identifier.uri	http://hdl.handle.net/123456789/546
dc.description	Supervised by Prof. Dr. Md. Hasanul Kabir Professor	en_US
dc.description.abstract	An effective and robust face descriptor is an essential component for a good facial expression recognition system. Many popular appearance-based meth- ods such as local binary pattern (LBP), local directional pattern (LDP) and lo- cal ternary pattern (LTP) have been proposed to serve this purpose and have been proven both accurate and efficient. During the last few years, many re- searchers have been providing significant effort and ideas to improve these methods. In this research work, we present a new face descriptor, Adaptive Robust Local Complete Pattern (ARLCP). ARLCP effectively encodes signifi- cant information of emotion-related features by using the sign, magnitude and directional information of edge response that is more robust to noise and illu- mination variation. In this histogram-based approach, obtained feature image is divided into several regions, histogram of each region is computed indepen- dently and all histograms are concatenated to generate a final feature vector. We have experimented our method on several datasets using cross-validation schemes to evaluate the performance. From those experiments, it is evident that our method(ARLCP) provides better accuracy in facial expression recog- nition.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering, Islamic University of Technology, Board Bazar, Gazipur, Bangladesh	en_US
dc.title	Adaptive Robust Local Complete Pattern For Facial Expression Recognition	en_US
dc.type	Thesis	en_US