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Facial Expression Recognition Using Subspace Learning On LBP

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dc.contributor.author asnem, Kazi Nuzhat T
dc.contributor.author Ahmed, Tazin
dc.date.accessioned 2019-04-01T10:23:00Z
dc.date.available 2019-04-01T10:23:00Z
dc.date.issued 2018-05-05
dc.identifier.uri http://dspace.ewubd.edu/handle/2525/3028
dc.description This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh. en_US
dc.description.abstract There is different types of methods that can recognize the facial expression but none of them were able to generate the accurate result due to the lack of generalizability. This field has a huge possibilities and can open new doors to human machine interaction. As a result the demand of recognizing the human expression correctly is increasing day by day. So there are many ways to recognize the facial expression. Here in this paper, we are trying to analyze the facial expression on different sub space. First we applied a conventional method, LBP. Then we tried to apply Principal Component Analysis (PCA). We tried another subspace algorithm called Kernel Principal Component Analysis. Then we compared the results. We compared the accuracy of recognizing facial expression of these two algorithm using BSVM tool. en_US
dc.language.iso en_US en_US
dc.publisher East West University en_US
dc.relation.ispartofseries ;CSE00182
dc.subject Facial Expression Recognition Using Subspace Learning On LBP en_US
dc.title Facial Expression Recognition Using Subspace Learning On LBP en_US
dc.type Thesis en_US


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