Deep Neural Network for Accurate Age Group Prediction through Pupil Using the Optimized UNet Model

Abstract

Predicting age automatically from the image is a difficult task and shortening the challenge to be more concise is also a challenging task. Nevertheless, the existing implementations using manually designed features using a wide variety of input features using benchmark datasets are unsatisfactory as they suffer from unknown subject information. It is challenging to judge CNN’s performance using such approaches. The proposed system performs the segmentation through UNet without using a dense layer to perform the segmentation and classification. The proposed system uses the skip connection to hold the loss at the max-pooling layer. Also, the morphological processing and probabilistic classification served as the proposed system’s novelty. The proposed method used three benchmark datasets, MMU, CASIA, and UBIRIS, to experiment with building a training model and tested using various optimization techniques to perform an accurate segmentation. To further test and improve the quality of the proposed method, we experimented with random images. The proposed system’s accuracy is 96% when experimented on random images of subjects collected purely for experimentation. Three optimizers, namely, Stochastic Gradient Descent, RMS Prop, and Adaptive Moment Optimizer, were experimented with in the proposed system to fit the system. The average accuracy we received using optimizers is 71.9, 84.3, and 96.0 for the loss value of 2.36, 2.30, and 1.82, respectively.