The research project Age and Gender Prediction focuses on human-computer interaction. In this project, we take into consideration the automatic age and gender prediction from face photos, which has recently received a lot of interest because to its numerous applications in various facial analysis issues. Unfortunately, the available models still fall short of the requisite accuracy level, which is required for the implementation of these models in real-world applications, because of the significant intraclass variance of face images (such as change in lighting, position, scale, and occlusion). In this study, the researcher offers a deep learning framework, based on the ensemble of attentional and residual convolutional networks, to predict gender and age group of facial images with high accuracy rate. By implementing attention mechanisms.