@inproceedings{scholars2823,
         address = {Kuala Lumpur},
           title = {Emotion detection using sub-image based features through human facial expressions},
             doi = {10.1109/ICCISci.2012.6297264},
          volume = {1},
            note = {cited By 12; Conference of 2012 International Conference on Computer and Information Science, ICCIS 2012 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2012 ; Conference Date: 12 June 2012 Through 14 June 2012; Conference Code:93334},
           pages = {332--335},
         journal = {2012 International Conference on Computer and Information Science, ICCIS 2012 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2012 - Conference Proceedings},
            year = {2012},
          author = {Mohan Kudiri, K. and Md Said, A. and Nayan, M. Y.},
            isbn = {9781467319386},
        keywords = {Classification rates; Detection performance; Emotion detection; Facial Expressions; Facial muscles; Human bodies; Human decision making; Human faces; Human facial expressions; Mel-frequency cepstral coefficients; Non-verbal communications; Real-world application; Relative sub-image based features, Feature extraction; Human computer interaction; Information science; Principal component analysis; Support vector machines; Technology, Classification (of information)},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867903363&doi=10.1109\%2fICCISci.2012.6297264&partnerID=40&md5=6d99f608ddc6456843137dae48f54442},
        abstract = {The human face is an important human body part which plays an extraordinary role in the human to human or human to machine communications. As such, it is important to design robust emotion detection system for real world applications like human decision making and effective human computer interaction. Facial expression provides non-verbal communication for human computer interactions. This study identifies the problem of loss of data in the feature extraction scheme based on limited number of positions of facial muscles. To improve detection performance, relative sub-image based features are proposed. Classifications have been done using the support vector machine to implement an automated emotion detection system for facial expressions. The results show that the proposed relative sub-image based features enhance the classification rates. {\^A}{\copyright} 2012 IEEE.}
}