Abstract
This study presents a novel approach to simultaneous gender and age recognition by including emotional context using a mask region-based convolution neural network (Mask R-CNN). The suggested approach makes exact gender and age categorization possible by using deep learning to examine facial traits and emotions. The model captures the complex relationship between emotional states and face characteristics by extending Mask R-CNN to include emotional signals. By enhancing overall system performance, this integration offers a more comprehensive comprehension of human behavior. The suggested method achieves state-of-the-art accuracy in gender and age recognition while capturing emotional nuances in facial expressions, as demonstrated by empirical data. This work advances the development of multimodal human-centric systems and has potential applications in a variety of domains, including surveillance, human-computer interaction, and tailored experience.
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