Real-Time Convolutional Neural Networks for Emotion and Gender Classification

Emotion and gender recognition are important areas of research in the field of computer vision and human-computer interaction. The proposed CNN architecture is designed to extract features from facial images and classify them into six basic emotions (happy, sorrow, anger, fear, surprise, and disgust) and two genders (male and female) in real-time. To extract and categorize characteristics from facial photographs, the suggested CNN architecture consists of convolutional layers, pooling layers, and fully connected layers. The suggested system performs at the cutting edge for both emotion and gender recognition tasks when tested on publicly accessible datasets. The proposed real- time CNN architecture has potential applications in various fields, including social robotics, human-computer interaction, and affective computing.