Naidoo, ATapamo, JRKhutlang, Rethabile2018-09-032018-09-032018-07Naidoo, A., Tapamo, J.R. and Khutlang, R. 2018. Affective Computing: Using Covariance Descriptors for Facial Expression Recognition. International Conference on Image and Signal Processing (ICISP 2018), 2-4 July 2018, Cherbourg, France978-3-319-94210-0https://link.springer.com/book/10.1007/978-3-319-94211-7https://link.springer.com/content/pdf/bfm%3A978-3-319-94211-7%2F1.pdfhttp://hdl.handle.net/10204/10397Copyright: 2018 Springer.Due to copyright restrictions, the attached PDF file contains the accepted version of the paper. For access to the published version, please consult the publisher's website.Facial expression contains a rich variety of affective information. Facial Expression Recognition (FER) is an emerging field that has broad applications in the fields of human-computer interaction, psychological behaviour analysis and image understanding. However, FER presently is not fully realized due to the lack of an effective facial feature descriptor. This paper will examine the performance of a novel facial image descriptor referred to as Local Directional Covariance Matrices (LDCM). The descriptor will consist of fusing features, such as location, intensity, filter responses and incorporate local texture patterns. Tests were done on both posed and spontaneous facial expression datasets to evaluate the performance of the proposed model on real-world applications. Results demonstrate the effectiveness of the covariance feature descriptors compared to standard methods.enLocal directional patternFacial expression recognitionCovariance descriptorLocal binary patternConference PresentationNaidoo, A., Tapamo, J., & Khutlang, R. (2018). Affective Computing: Using Covariance Descriptors for Facial Expression Recognition. Springer. http://hdl.handle.net/10204/10397Naidoo, A, JR Tapamo, and Rethabile Khutlang. "Affective Computing: Using Covariance Descriptors for Facial Expression Recognition." (2018): http://hdl.handle.net/10204/10397Naidoo A, Tapamo J, Khutlang R, Affective Computing: Using Covariance Descriptors for Facial Expression Recognition; Springer; 2018. http://hdl.handle.net/10204/10397 .TY - Conference Presentation AU - Naidoo, A AU - Tapamo, JR AU - Khutlang, Rethabile AB - Facial expression contains a rich variety of affective information. Facial Expression Recognition (FER) is an emerging field that has broad applications in the fields of human-computer interaction, psychological behaviour analysis and image understanding. However, FER presently is not fully realized due to the lack of an effective facial feature descriptor. This paper will examine the performance of a novel facial image descriptor referred to as Local Directional Covariance Matrices (LDCM). The descriptor will consist of fusing features, such as location, intensity, filter responses and incorporate local texture patterns. Tests were done on both posed and spontaneous facial expression datasets to evaluate the performance of the proposed model on real-world applications. Results demonstrate the effectiveness of the covariance feature descriptors compared to standard methods. DA - 2018-07 DB - ResearchSpace DP - CSIR KW - Local directional pattern KW - Facial expression recognition KW - Covariance descriptor KW - Local binary pattern LK - https://researchspace.csir.co.za PY - 2018 SM - 978-3-319-94210-0 T1 - Affective Computing: Using Covariance Descriptors for Facial Expression Recognition TI - Affective Computing: Using Covariance Descriptors for Facial Expression Recognition UR - http://hdl.handle.net/10204/10397 ER -