Deep convolutional neural networks for age and gender estimation using an imbalanced dataset of human face images

Automatic age and gender estimation provides an important information to analyze real-world applications such as human–machine interaction, system access, activity recognition, and consumer profile detection. While it is easy to estimate a person’s gender from human facial images, estimating their age is difficult. In such previous challenging studies, traditional convolutional neural network (CNN) methods have been used for age and gender estimation. With the development of deep convolutional neural network (DCNN) architectures, more successful results have been obtained than traditional CNN methods. In this study, two state-of-the-art DCNN models have been developed in the field of artificial intelligence (AI) to make age and gender estimation on an imbalanced dataset of human face images. Firstly, a new model called fast description network (FINet) was developed, which has a parametrically changeable structure. Secondly, the number of parameters has been reduced by using the layer reduction approach in InceptionV3 and NASNetLarge DCNN model structures, and a second model named inception Nasnet fast identify network (INFINet) was developed by concatenating these models and the FINet model as a triple. FINet and INFINet models developed for age and gender estimation were compared with many other state-of-the-art DCNN models in AI. The most successful accuracy results in terms of both age and gender were obtained with the INFINet model (age: 61.22%, gender: 80.95% in the FG-NET dataset, age: 72.00%, gender: 90.50% in the UTKFace dataset). The results obtained in age and gender estimation with the INFINet model are much more effective than other recent state-of-the-art works. In addition, the FINet model, which has a much smaller number of parameters than the compared models, showed a classification performance that can compete with state-of-the-art methods for age and gender estimation. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.