3D HMM-based Facial Expression Recognition using Histogram of Oriented Optical Flow
Journal Title: Transactions on Machine Learning and Artificial Intelligence - Year 2015, Vol 3, Issue 6
Abstract
In this paper, we propose a 3D HMM (Three-dimensional Hidden Markov Models) approach to recognizing human facial expressions and associated emotions. Human emotion is usually classified by psychologists into six categories: Happiness, Sadness, Anger, Fear, Disgust and Surprise. Further, psychologists categorize facial movements based on the muscles that produce those movements using a Facial Action Coding System (FACS). We look beyond pure muscle movements and investigate facial features – brow, mouth, nose, eye height and facial shape – as a means of determining associated emotions. Histogram of Optical Flow is used as the descriptor for extracting and describing the key features, while training and testing are performed on 3D Hidden Markov Models. Experiments on datasets show our approach is promising and robust.
Authors and Affiliations
Sheng H. Kung, Mohamed Zohdy, Djamel Bouchaffra
A New Approach for Query Processing and Optimization Base on the Fuzzy Object Algebra and Equivalent Transformation Rules
For enhancing the effeciency of processing users’ queries, all Database Management Systems (DBMSs) must conduct query pre-processing, or query optimizing. This paper proposes a new model for the Fuzzy Object Oriented DBM...
Resources Indexation-Based E-Learning System
We propose in this article a study that illustrates the techniques used to represent the educational objects which serve to facilitate their reuse. We will enrich this study by taking into account the semantics of the co...
Dialogue Based Decision Making in Online Trading
Software agents, acting on behalf of humans, have been identified as an important solution for future electronic markets. Such agents can make their own decisions based on given prior preferences and the market environme...
Learning Style Classification Based on Student's Behavior in Moodle Learning Management System
In learning field, each student has his own learning style that affects his way of get, process, understand and percept information. Determining the learning style of students enhances the performance of learning process...
Support Vector Machine Regression and Artificial Neural Network for Channel Estimation of LTE Downlink in High-Mobility Environments
In this paper we apply and assess the performance of support vector machine regression (SVR) and artificial neural network (ANN) channel estimation algorithms to the reference signal structure standardized for LTE Downli...