Abstract

Convolutional Neural Networks (CNNs) are used in this study to determine age, gender, and emotion in face analysis. Using a variety of datasets for training, the age diagnosis component accurately predicts age categories by identifying small changes in face features associated with ageing using CNNs' hierarchical feature learning. Large datasets were used to train the gender detection module, which helps facial recognition and marketing applications by automatically extracting gender-specific traits for accurate gender identification. Sentiment analysis and human-computer interaction benefit from the emotion detection framework's improved emotion classification, which is achieved by capturing temporal and spatial correlations in facial expressions. CNNs are effective for face analysis, as evidenced by the model's adaptability and robustness across a range of demographics, as well as by testing and training on a variety of datasets. This paper provides a thorough framework for age, gender, and emotion recognition based on CNN, which holds promise for breakthroughs in a number of sectors depending on facial analysis.