Open AccessFacial expression classification using Cross Diagonal Neighborhood Pattern
Author(s) -
A. Obulesu,
R Keerthi
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1228/1/012055
Subject(s) - convolutional neural network , diagonal , artificial intelligence , computer science , pattern recognition (psychology) , face (sociological concept) , facial expression , feature extraction , field (mathematics) , feature (linguistics) , expression (computer science) , facial recognition system , computer vision , mathematics , social science , linguistics , philosophy , geometry , sociology , pure mathematics , programming language
Facial Expression Recognition has significant applications in the field of Affective Computing. FER has its significant contribution in the fields like human computer interaction, neurology, psychiatry, image processing, computer vision, affective computing, and information security. This work gives unique and robust FER System by extracting unique and robust face features. This work proposes Cross Diagonal Neighborhood Patterns (CDNP) for unique feature extraction. The CDNP features are further processed by Gray Level Co-occurrence Matrix (GLCM). The derived CDNP-GLCM features are sent to Convolutional Neural Network (CNN) to train various expressions.