Facial Expression Recognition Using a Hybrid Kernel Based Extreme Learning Machine
A bunch of information is conveyed by human beings in the form of facial expression apart from just what is spoken. Automatic recognition of facial expressions could be a significant component of natural human-machine interfaces and it may also be used in behavioural science and in
medical practice, which helps to ease the communication. This ability can serve in many contexts. In this paper, an efficient Hybridization of Adaptive Kernel function based Extreme Learning Machine with Chicken Swarm Optimization (HAKELM-CSO) algorithm is used for identifying the facial expression.
Initially, the input image is pre-processed for noise removal and face detection is done using wiener filter. To recognize the skin region Quantum Evolutionary Algorithm (QEA) is used. The feature extraction is done through Grey-level co-occurrence matrix (GLCM) and the feature selection is
made by Modified Firefly (MF) algorithm. These features are fed to HAKELM to classify the universal facial expressions. HAKELM learning algorithm with CSO approach is used for optimizing the kernel functions parameters. The experimental results shows that the proposed method performs better
than existing Support Vector Machine (SVM) and Principle Component Analysis (PCA) methods in terms of the performance metrics such as ROC, Precision, Recall, F-Measure, accuracy and processing time by using MATLAB. The proposed method was evaluated with the images of Indian Face Database and
Japanese Female Face Database.
Keywords: Chicken Swarm Optimization; Facial Expression; Hybrid Kernel Based Extreme Learning Machine; MF Algorithm
Document Type: Research Article
Affiliations: 1: EIE, National Engineering College, Kovilpatti 628503, Tamilnadu, India 2: Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai 600062, India
Publication date: 01 June 2017
- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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