A Lightweight Convolutional Neural Network for Real and Apparent Age Estimation in Unconstrained Face Images

Real and apparent age estimation of human face has attracted increased attention due to its numerous real-world applications. Different intelligent application scenarios can benefit from these computer-based systems that predict the ages of people correctly. Automatic apparent age system is particularly useful in medical diagnosis, facial beauty product development, movie role casting, the effect of plastic surgery, and anti-aging treatment. Predicting the real and apparent age of people has been quite difficult for both machines and humans. More recently, Deep learning with Convolutional Neural Networks (CNNs) methods have been extensively used for these classification task. It has incomparable advantages in extracting discriminative image features from human faces. However, many of the existing CNN-based methods are designed to be deeper and larger with more complex layers that makes it challenging to deploy on mobile devices with resource-constrained features. Therefore, we design a lightweight CNN model of fewer layers to estimate the real and apparent age of individuals from unconstrained real-time face images that can be deployed on mobile devices. The experimental results, when analyzed for classification accuracy on FG-NET, MORPH-II and APPA-REAL, with large-scale face images containing both real and apparent age annotations, show that our model obtains a state-of-the-art performance in both real and apparent age classification when compared to state-of-the-art methods. The new results and model size, therefore, confirm the usefulness of the model on resource-constrained mobile devices.