DeepComp: A Deep Comparator for Improving Facial Age-Group Estimation

Abstract

We introduce an age-group estimation scheme known as DeepComp. It is a combination of an Early Information-Sharing Feature Aggregation (EISFA) mechanism and a ternary classifier. The EISFA part is a feature extractor that applies a siamese layer to input images and an aggregation module that sums up all the images. The ternary process compares the image representations into three possible outcomes corresponding to younger, similar, or older. From the comparisons, we arrive at a score indicating the similarity between an input and reference images: the higher the score, the closer the similarity. Experimentation shows that our DeepComp scheme achieves an impressive 94.9% accuracy on the Adience benchmark dataset using a minimum number of reference images per age group. Moreover, we demonstrate the generality of our method on the MORPH II dataset, and the result is equally impressive. Altogether, we show that, among other schemes, our method exemplifies facial age-group estimation.