Abstract
The development of intelligent systems capable of effectively learning and recognising objects is the major goal of the very active research fields of pattern recognition and automatic classification. The usage of biometrics, which is generally utilised for security considerations, is integral to these applications. In the realm of research, the face modality has grown in importance as a basic biometric technique. The objective of this work is to create a system for estimating age and gender from a facial image or real-time video using convolutional neural networks. In this study, three CNN network models with various architectures (number of filters, number of convolution layers, etc.) were built and validated using data from the IMDB and WIKI. The results revealed that CNN networks significantly increase the system's performance and recognition accuracy.
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