PCA + LDA Fuzzy Based Model for Emotional Nature Recognition of Human Video
Dhiren Pandit1, Jayesh Dhodiya2
Dr. Dhiren Pandit, Dept. of Mathematics & Humanities, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India.
Dr. Jayesh Dhodiya, Applied Mathematics & Humanities Department, SVNIT, Surat, Gujarat, India.
Third Author name, His Department Name, University/ College/ Organization Name, City Name, Country Name.
Manuscript received on 11 August 2019. | Revised Manuscript received on 11 August 2019. | Manuscript published on 30 September 2019. | PP: 242-246 | Volume-8 Issue-3 September 2019 | Retrieval Number: C3955098319/19©BEIESP | DOI: 10.35940/ijrte.C3955.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Human expresses their emotions by means of verbal and nonverbal communication. Nonverbal communications are done mainly using facial expression. This paper aims to recognize human emotion using nonverbal communication of human facial expressions. Different mathematical techniques like: principle component analysis (PCA), linear discriminate analysis (LDA) and independent component analysis (ICA) are widely used for human facial expression recognition. This paper applied fusion of PCA and LDA based model for facial video emotion recognition with neural network (NN), fuzzy approach and Ekman’s proposed concept of action units of faces. Moreover, results obtained in linguistic form using action units with fuzzy approach on unknwn individual persons for identification of nature of input video and compare with the actual data to validate the model. This paper concludes that developed approach provides 99% accuracy for human facial expression recognition and identification of nature of input video.
Index Terms: Action Units, Fuzzy Approach, LDA, Neural Network, PCA.
Scope of the Article: Fuzzy Logics