Research Article
Novel Deep Learning Techniques to Design the Model and Predict Facial Expression, Gender, and Age Recognition
@INPROCEEDINGS{10.1007/978-3-031-35081-8_29, author={N. Sujata Gupta and Saroja Kumar Rout and Viyyapu Lokeshwari Vinya and Koti Tejasvi and Bhargavi Rani}, title={Novel Deep Learning Techniques to Design the Model and Predict Facial Expression, Gender, and Age Recognition}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part II}, proceedings_a={ICISML PART 2}, year={2023}, month={7}, keywords={Convolution Neural Network Haar-Cascade Classifier Facial expression Emotion}, doi={10.1007/978-3-031-35081-8_29} }- N. Sujata Gupta
Saroja Kumar Rout
Viyyapu Lokeshwari Vinya
Koti Tejasvi
Bhargavi Rani
Year: 2023
Novel Deep Learning Techniques to Design the Model and Predict Facial Expression, Gender, and Age Recognition
ICISML PART 2
Springer
DOI: 10.1007/978-3-031-35081-8_29
Abstract
For computer and human interaction, human facial recognition is crucial. Our goal is to anticipate the expression of a human face, gender, and age as quickly and accurately as possible in real-time. Understanding human behavior, detecting mental diseases, and creating synthetic human expressions are only a few of the applications of automatic human facial recognition . Salespeople can employ age, gender, and emotional state prediction to help them better understand their consumers. Convolutional Neural Network one of the Deep Learning techniques is utilized to design the model and predict emotion, age, and gender, using the Haar-Cascade frontal face algorithm to detect the face. This model can predict from video in real-time. The goal is to create a web application that uses a camera to capture a live human face and classify it into one of seven expressions, two ages, and eight age groups. The process of detecting face, pre-processing, feature extraction, and the prediction of expression, gender, and age is carried out in steps.
