Supervised Machine Learning Techniques based Human Age, Gender and Emotion Detection
Anup Bhange, Ankush Sondal, Mansi Chandel, Vishal Mishra, Bipul Ranjan, Harsh Gupta · International Journal of Advanced Research in Science Communication and Technology · 2022
The proposed work is “Supervised machine learning based Human Age, Gender and Emotion Detector” is a project to help people avoid being victims of frauds. Automated age and gender detection has been generally used in our daily lives that we come across, majorly in a person to computer interaction, visual surveillance, biometric analysis, electronics and other applications of commercial use. By recognizing the emotions of a person, we can improve the recommendation system. The existing methods have quite satisfying performance on real-world images if facial expressions of input image is neutral or calm, it lacks significantly in age prediction when facial expressions are altered. For image classification, a convolutional neural networks (CNNs) pre=trained are used on ImageNet from Caffe, a modifiable platform for state-of-the-art deep learning algorithms and a set of reference models. The YOLOv3 (You Only Look Once V3) algorithm was employed for such purposed having a desirable ability to serve the required purpose.