Emotional Expression Recognition using Support Vector Machines
نویسنده
چکیده
The objective of this paper is to apply Support Vector Machines to the problem of classifying emotion on images of human faces. This welldefined problem is complicated by the natural variation in people’s faces, requiring the classification algorithm to distinguish the small number of relevant features from the large pool of input features. Recent experimentation using neural networks has achieved over 85% classification accuracy. These experiments provide a metric for evaluation of the Support Vector Machine technique, which was shown to have equivalent performance to neural networks.
منابع مشابه
Face Recognition using Eigenfaces , PCA and Supprot Vector Machines
This paper is based on a combination of the principal component analysis (PCA), eigenface and support vector machines. Using N-fold method and with respect to the value of N, any person’s face images are divided into two sections. As a result, vectors of training features and test features are obtain ed. Classification precision and accuracy was examined with three different types of kernel and...
متن کاملMultimodal Emotion Recognition Integrating Affective Speech with Facial Expression
In recent years, emotion recognition has attracted extensive interest in signal processing, artificial intelligence and pattern recognition due to its potential applications to human-computer-interaction (HCI). Most previously published works in the field of emotion recognition devote to performing emotion recognition by using either affective speech or facial expression. However, Affective spe...
متن کاملWavelets-based facial expression recognition using a bank of support vector machines
A human face does not play its role in the identification of an individual but also communicates useful information about a person’s emotional state at a particular time. No wonder automatic face expression recognition has become an area of great interest within the computer science, psychology, medicine, and human– computer interaction research communities. Various feature extraction technique...
متن کاملA Comparative Study of Extreme Learning Machines and Support Vector Machines in Prediction of Sediment Transport in Open Channels
The limiting velocity in open channels to prevent long-term sedimentation is predicted in this paper using a powerful soft computing technique known as Extreme Learning Machines (ELM). The ELM is a single Layer Feed-forward Neural Network (SLFNN) with a high level of training speed. The dimensionless parameter of limiting velocity which is known as the densimetric Froude number (Fr) is predicte...
متن کاملApplications of Support Vector Machines on Smart Phone Systems for Emotional Speech Recognition
An emotional speech recognition system for the applications on smart phones was proposed in this study to combine with 3G mobile communications and social networks to provide users and their groups with more interaction and care. This study developed a mechanism using the support vector machines (SVM) to recognize the emotions of speech such as happiness, anger, sadness and normal. The mechanis...
متن کامل