A Novel Approach for Age and Gender Detection using Deep Convolution Neural Network

This research paper proposes a novel approach for age and gender detection using a deep convolutional neural network (DCNN). The increasing prevalence of age and gender classification in applications, particularly due to the expansion of social platforms and media, is noted. Traditional techniques employ hand-crafted features and classifiers with limitations in both accuracy and scalability. This study shows that significant improvements in task execution can be achieved through learning representations using convolutional neural networks (CNNs). In this study, we fine-tune a pre-trained DCNN on a large-scale UTKFace dataset and evaluate its performance on the same. The proposed approach in the study outperforms state-of-the-art methods in terms of accuracy and computational efficiency, as demonstrated by the results.