• sanjay
• vasanth kumar

This study introduces a real-time deep learning-based system for the analysis of faces and there cognition of age, gender, and facial emotional expressions. Convolution neural networks (CNN) are used in the proposed method to extract distinguishing features from facial photographs. By learning hierarchical representations of facial traits, the CNN architecture is able to recognize intricate patterns and changes in expression, age, and gender. By utilizing the computational efficiency of GPU acceleration and improved parallel processing, the system achieves real-time performance. The usefulness of the suggested approach is demonstrated by experimental findings using public face expression, age, and gender datasets, which show high accuracy in tasks for emotion classification, age estimation, and gender prediction. The system can also conduct face analysis tasks including facial landmark detection and face alignment, which increase its applicability for a variety of applications like social robotics, affective computing, and human-computer interaction .In general, the suggested real-time deep recognizer offers a reliable and effective solution for face, age, and gender analysis.

"Facial recognition of age gender emotions in deep learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.c295-c302, March-2024, Available :http://www.jetir.org/papers/JETIR2403237.pdf

"Facial recognition of age gender emotions in deep learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppc295-c302, March-2024, Available at : http://www.jetir.org/papers/JETIR2403237.pdf