Recognizing Emotions from Facial Expressions Using Neural Network

Abstract

Recognizing the emotional state of a human from his/her facial gestures is a very challenging task with wide ranging applications in everyday life. In this paper, we present an emotion detection system developed to automatically recognize basic emotional states from human facial expressions. The system initially analyzes the facial image, locates and measures distinctive human facial deformations such as eyes, eyebrows and mouth and extracts the proper features. Then, a multilayer neural network is used for the classification of the facial expression to the proper emotional states. The system was evaluated on images of human faces from the JAFFE database and the results gathered indicate quite satisfactory performance.

Emotion Detection Using Machine Learning: An Analytical Review

“…This neural network offers improved training flexibility. Effectively tackles the challenges associated with classification [44]. MLPNNs are specifically designed as feed-forward networks making them well-suited for classifying problems involving multiple classes.…”

A simple multi-feature classification to recognize human emotions in Images

“…), supplied facial features as inputs to a single backpropagation neural network and classified the results using data about the eyes and mouth [5]. some researchers have used even Sobel filters to emphasize prominent edges and extracted features using pixel density [6]. Work by Konar and Chakraborty [7] to good results, as shown in Sect.…”

A simple multi-feature classification to recognize human emotions in Images

“…), supplied facial features as inputs to a single backpropagation neural network and classified the results using data about the eyes and mouth [5]. Some researchers have used Sobel filters to emphasize prominent edges and extracted features using pixel density [6]. Work by Konar and Chakraborty [7]…”

“…In these trials, the N N n networks were the same feed forward {N N 1 :(30,10), N N 2 :(30,10), N N 3 : (6,3), N N 4 : (6,3), N N 5 :(48,20)} but the F N network was a radial basis network. Table II shows the results performed with images preprocessed under two categories of thresholds.…”