Facial Emotions Recognition using Convolutional Neural Network

M. Aravind Kumar

ISBN 10: 6206152278 / ISBN 13: 9786206152279

Published by LAP LAMBERT Academic Publishing Mär 2023, 2023

About this Item

Neuware -This book summarizes Facial expression recognition and identifies emotion from a face image, it is a manifestation of the activity and personality of a human. In the 20th century, the American psychologist Ekman and Friesen defined six basic emotions (anger, fear, disgust, sadness, surprise, and happiness), which are the same across cultures. Facial expression recognition has brought much attention in the past years due to its impact on clinical practice, social robotics, and education. According to diverse research, emotion plays an important role in education. we presented a Convolutional Neural Network model for students¿ facial expression recognition. The proposed model includes 4 convolutional layers, 4 max-pooling, and 2 fully connected layers. The system recognizes faces from students' input images using a Haar-like detector and classifies them into seven facial expressions: surprise, fear, disgust, sad, happy, anger, and neutral.Books on Demand GmbH, Überseering 33, 22297 Hamburg 64 pp. Englisch.

Seller Inventory # 9786206152279

Bibliographic Details

• Title: Facial Emotions Recognition using Convolutional Neural Network
• Publisher: LAP LAMBERT Academic Publishing Mär 2023
• Publication Date: 2023
• Language: English
• ISBN 10: 6206152278
• ISBN 13: 9786206152279
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 113 grams

About this title

Synopsis

This book summarizes Facial expression recognition and identifies emotion from a face image, it is a manifestation of the activity and personality of a human. In the 20th century, the American psychologist Ekman and Friesen defined six basic emotions (anger, fear, disgust, sadness, surprise, and happiness), which are the same across cultures. Facial expression recognition has brought much attention in the past years due to its impact on clinical practice, social robotics, and education. According to diverse research, emotion plays an important role in education. we presented a Convolutional Neural Network model for students’ facial expression recognition. The proposed model includes 4 convolutional layers, 4 max-pooling, and 2 fully connected layers. The system recognizes faces from students' input images using a Haar-like detector and classifies them into seven facial expressions: surprise, fear, disgust, sad, happy, anger, and neutral.