Human Age Estimation and Gender Classification Using Deep Convolutional Neural Network

At present age estimation and gender classification task has achieved a great importance due to analyzing the category of people in social media, business, customers’ choice etc. Automatic age and gender classification task from analyzing facial images has become a concern to this competitive world. In this paper, we have proposed to apply transfer learning technique on facial images of people of different ages and gender. Age and gender are special attributes which can be extracted from facial images. A deep convolutional neural network is trained using our target dataset to achieve a good classification performance. We have evaluated the classification performance on Adience benchmark for age and gender estimation using ResNet50, VGG16, VGG14 and VGG17 deep CNN models. Using an ensemble technique (majority voting) of these (VGG) classifiers, we have found approximately 90% classification accuracy on age estimation task. We have also found 94% 1-off age classification accuracy using VGG14.