Title :
A probabilistic approach to support Self-Organizing Map (SOM) driven facial expression recognition
Author :
Chowdhury, Muhammad Iqbal Hasan ; Alam, Fahim Irfan
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of Chittagong, Chittagong, Bangladesh
Abstract :
Automated understanding of human facial expression is an active and concerning research topic. It is expected that in near future full-fledged understanding of human facial expression will enable machines to behave more intelligently. In this paper we proposed a system for automatic facial expression recognition. A consistent combination of Self-Organizing Map (SOM), Learning Vector Quantization (LVQ) and Naïve Bayes classifier is developed to recognize facial expression from Cohn Kanade (CK) and Japanese Female Facial Expression (JAFFE) database. Satisfactory experimental results yield the possibility of using this system in real world applications. Proposed methodology shows an accuracy rate of 81.5% for CK dataset and 87.2% accuracy rate for JAFFE dataset.
Keywords :
Bayes methods; emotion recognition; face recognition; image classification; learning (artificial intelligence); self-organising feature maps; vector quantisation; CK database; Cohn Kanade database; JAFFE database; Japanese Female Facial Expression database; LVQ; SOM-driven facial expression recognition; automated human facial expression understanding; automatic facial expression recognition; learning vector quantization; naïve Bayes classifier; probabilistic approach; support self-organizing map driven facial expression recognition; Classification algorithms; Face; Face recognition; Feature extraction; Gold; Support vector machine classification; Testing; Facial Expression Recognition; Learning Vector Quantization (LVQ); Local Binary Pattern (LBP); Naïve Bayes Classifier; Self-organizing Map (SOM);
Conference_Titel :
Computer and Information Technology (ICCIT), 2014 17th International Conference on
DOI :
10.1109/ICCITechn.2014.7073131
