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Abstract: Facial attributes are crucial in numerous applications such as access control and video surveillance, where demographic data like age and gender can be inferred from facial images. Automatic estimation of age and gender enables tailored content de- livery and personalized services. However, ex- tracting effective features from facial images poses a significant challenge. This paper pro- poses employing Convolutional Neural Net- works (CNNs) for automatic age and gender prediction. CNNs have demonstrated ground-breaking success in face recognition and im- age classification tasks. Leveraging pre-trained deep CNNs, this research aims to estimate age and gender accurately from facial images. The methodology involves utilizing convolution layers to produce a robust and compact output, enhancing the efficiency of age and gender detection systems.
Abhinav Singh (Wed,) studied this question.