Face-Based Age and Gender Estimation Using Improved Convolutional Neural Network Approach

Age and gender estimation using face images by working on the facial features of the human, which are unique for each person can serve several applications such as person’s identification, access control, human–machine interaction, to avoid any type of fraud or misuse of someone’s identity, forensic, and in organizations. However, previous work for age estimation was based on handcraft features to encode the ageing patterns. With the advancement of deep architectures in CNN algorithms, CNN shows better performance than handcraft features. There exist several methods and relatively substantial literature on the field. However, biological variations and uncertainty will always be associated with age estimates because of the large variety in facial appearance and several other extrinsic and intrinsic factors. Thus, the proposed work is based on age and gender estimation using an improved convolutional neural network (CNN). The proposed model improves the computational performance, and it is generalized to another dataset also. The convolutional layer is the fundamental building block of a CNN. Like other neural networks, CNNs consist of neurons that are capable of learnable weights and biases. The proposed model is evaluated using UTKFace, IMDB-WIKI, FG-NET, CACD datasets. The efficacy of the proposed algorithm in comparison to existing algorithms achieves better accuracy in age estimation through extensive simulations. The proposed model is trained on UTKFace (aligned and cropped faces) dataset has shown 94.01% accuracy for age and 99.86% accuracy for gender estimation.