Face-Based Age and Gender Classification Using Deep Learning Model

Age and gender classification of human's face is an important research focus, having many application areas. Recently, Convolutional Neural Networks (CNNs) model has proven to be the most suitable method for the classification task, especially of unconstrained real-world faces. This could be as a result of its expertise in feature extraction and classification of face images. Availability of both high-end computers and large training data also contributed to its usage. In this paper, we, therefore, propose a novel CNN-based model to extract discriminative features from unconstrained real-life face images and classify those images into age and gender. We approach the large variations attributed to those unconstrained real-life faces with a robust image preprocessing algorithm and a pretraining on a large IMDb-WIKI dataset containing noisy and unfiltered age and genders labels. We also adopted a dropout and data augmentation regularization method to overcome the risk of overfitting and allow our model generalize on the test images. We show that well-designed network architecture and properly tuned training hyperparameters, give better results. The experimental results on OIU-Adience dataset confirm that our model outperforms other studies on the same dataset, showing significant performance in terms of classification accuracy. The proposed method achieves classification accuracy values of 84.8% on age group and classification accuracy of 89.7% on gender.