GENDER RECOGNITION THROUGH FACE IMAGES USING CONVOLUTIONAL NEURAL NETWORKS
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Abstract
Gender recognition from facial images is widely used in the field of computer vision for surveillance which is easy for a human but challenging for machines. In computer vision, some factors such as differences in illumination, angle, occlusion, and expressions cause the problem of low accuracy. So, a Convolutional Neural Network (CNN) based transfer learning method is being applied in this work since CNN performs automatic feature extraction and learns complex high-level features in image classification applications. The proposed method efficiently classifies gender classes (male and female) on ‘Adience’: a benchmark face image dataset publicly available on the Kaggle website. This work interrogates seven different pre-trained CNNs used as base networks in the proposed transfer learning method. For the transfer of the pre-acquired knowledge, five different sets of layers (named experiments-1 to 5) have been tested with every model, and their performance is evaluated. Among these, experiment-4 has performed best with all models. However, DenseNet-121 has achieved the highest mean accuracy of 92.83% with a set of layers in experiment-4. We evaluate the performance of the proposed model using five-fold cross-validation, and the final mean accuracy is compared with previous methods in the literature
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