A Cascaded Convolutional Neural Network for Age Estimation of Unconstrained Faces

Amit Kumar

Abstract: We propose a coarse-to-fine approach for estimating the apparent age from unconstrained face images using deep convolutional neural networks (DCNNs). The proposed method consists of three modules. The first one is a DCNN-based age group classifier which classifies a given face image into age groups. The second module is a collection of DCNN-based regressors which compute the fine-grained age estimate corresponding in each age class. Finally, any erroneous age prediction is corrected using an error-correcting mechanism. Experimental evaluations on three publicly available datasets for age estimation show that the proposed approach is able to reliably estimate the age; in addition, the coarse-to-fine strategy and the error correction module significantly improve the performance.