Open AccessFacial Expression Recognition using SVM With CNN and Handcrafted Features
Author(s) -
Priyanka Ganesan,
S. Pavithra
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d7802.118419
Subject(s) - computer science , support vector machine , local binary patterns , convolutional neural network , facial expression , artificial intelligence , expression (computer science) , histogram of oriented gradients , benchmark (surveying) , histogram , set (abstract data type) , pattern recognition (psychology) , facial expression recognition , conversation , moment (physics) , machine learning , image (mathematics) , facial recognition system , geodesy , geography , programming language , linguistics , philosophy , physics , classical mechanics
The facial expression recognition system is playing vital role in many organizations, institutes, shopping malls to know about their stakeholders’ need and mind set. It comes under the broad category of computer vision. Facial expression can easily explain the true intention of a person without any kind of conversation. The main objective of this work is to improve the performance of facial expression recognition in the benchmark datasets like CK+, JAFFE. In order to achieve the needed accuracy metrics, the convolution neural network was constructed to extract the facial expression features automatically and combined with the handcrafted features extracted using Histogram of Gradients (HoG) and Local Binary Pattern (LBP) methods. Linear Support Vector Machine (SVM) is built to predict the emotions using the combined features. The proposed method produces promising results as compared to the recent work in [1].This is mainly needed in the working environment, shopping malls and other public places to effectively understand the likeliness of the stakeholders at that moment.