New Approach of Estimating Sarcasm Based on the Percentage of Happiness of Facial Expression Using Fuzzy Inference System

The procedure of determining whether micro expressions are present is accorded a high priority in the majority of settings. This is due to the fact that despite the best attempts of the person, these expressions will always expose the genuine sentiments that are buried under the surface. The purpose of this study is to provide a novel approach to the problem of measuring sarcasm by using a fuzzy inference system. The method involves analysing a person's facial expressions to evaluate the degree to which they are taking pleasure in something. It is feasible to distinguish five separate areas of a person's face, and precise active distances may be determined from the outline points of each of these regions. This category includes the brows on both sides of the face, as well as the eyes and lips. In order to arrive at a representation of an individual's degree of happiness while working within the parameters of the fuzzy inference system that has been provided, membership functions are first applied to computed distances. After that, the findings from the membership functions are put to use in yet another membership function so that an estimate of the sarcasm percentage may be derived from them. The suggested method is validated by using photos of human faces taken from the SMIC, SAMM, and CAS(ME) 2 datasets, which are the industry standards. This helps to guarantee that the method is effective

An Analysis of Facial Expression Recognition Techniques

In present era of technology , we need applications which could be easy to use and are user-friendly , that even people with specific disabilities use them easily. Facial Expression Recognition has vital role and challenges in communities of computer vision, pattern recognition which provide much more attention due to potential application in many areas such as human machine interaction, surveillance , robotics , driver safety, non- verbal communication, entertainment, health- care and psychology study. Facial Expression Recognition has major importance ration in face recognition for significant image applications understanding and analysis. There are many algorithms have been implemented on different static (uniform background, identical poses, similar illuminations ) and dynamic (position variation, partial occlusion orientation, varying lighting )conditions. In general way face expression recognition consist of three main steps first is face detection then feature Extraction and at last classification. In this survey paper we discussed different types of facial expression recognition techniques and various methods which is used by them and their performance measures

Facial expression recognition based on a mlp neural network using constructive training algorithm

International audienceThis paper presents a constructive training algorithm for Multi Layer Perceptron (MLP) applied to facial expression recognition applications. The developed algorithm is composed by a single hidden-layer using a given number of neurons and a small number of training patterns. When the Mean Square Error MSE on the Training Data TD is not reduced to a predefined value, the number of hidden neurons grows during the neural network learning. Input patterns are trained incrementally until all patterns of TD are presented and learned. The proposed MLP constructive training algorithm seeks to find synthesis parameters as the number of patterns corresponding for subsets of each class to be presented initially in the training step, the initial number of hidden neurons, the number of iterations during the training step as well as the MSE predefined value. The suggested algorithm is developed in order to classify a facial expression. For the feature extraction stage, a biological vision-based facial description, namely Perceived Facial Images PFI has been applied to extract features from human face images. To evaluate, the proposed approach is tested on three databases which are the GEMEP FERA 2011, the Cohn-Kanade facial expression and the facial expression recognition FER-2013 databases. Compared to the fixed MLP architecture and the literature review, experimental results clearly demonstrate the efficiency of the proposed algorithm

Facial expression recognition based on a mlp neural network using constructive training algorithm

International audienceThis paper presents a constructive training algorithm for Multi Layer Perceptron (MLP) applied to facial expression recognition applications. The developed algorithm is composed by a single hidden-layer using a given number of neurons and a small number of training patterns. When the Mean Square Error MSE on the Training Data TD is not reduced to a predefined value, the number of hidden neurons grows during the neural network learning. Input patterns are trained incrementally until all patterns of TD are presented and learned. The proposed MLP constructive training algorithm seeks to find synthesis parameters as the number of patterns corresponding for subsets of each class to be presented initially in the training step, the initial number of hidden neurons, the number of iterations during the training step as well as the MSE predefined value. The suggested algorithm is developed in order to classify a facial expression. For the feature extraction stage, a biological vision-based facial description, namely Perceived Facial Images PFI has been applied to extract features from human face images. To evaluate, the proposed approach is tested on three databases which are the GEMEP FERA 2011, the Cohn-Kanade facial expression and the facial expression recognition FER-2013 databases. Compared to the fixed MLP architecture and the literature review, experimental results clearly demonstrate the efficiency of the proposed algorithm