A Study of Convolutional Sparse Feature Learning for Human Age Estimate
Xiaolong Wang, Robert Li, Yin Zhou, Chandra Kambhamettu · 2017
Human age estimation plays an important role inhuman facial image analysis. Aging feature representation is one of the widely studied problems in this topic. Convolutional map (bio-inspired features, or BIF) has been proven to be the most successful framework, but its manual crafted filters cannot easily capture the complicated facial aging pattern. In this paper, we adopt this convolutional map framework but propose a novel feature learning approach based on convolutional sparse coding (CSC) that can automatically learn to characterize aging signatures. Compared to other popular feature learning approaches like deep convolutional neural network (CNN), we verify that our learning approach can extract localized subtle aging features like CNN, and also significantly reduce the model size. Moreover, we employ the standard deviation (STD)pooling to summarize the aging feature. Finally, the extracted features are fed into a discriminative manifold learning model to obtain more discriminative low-dimensional representations and further improve the computational efficiency. We evaluate our approach over the standard benchmark datasets. The experimental results demonstrate that our approach impressively out performs the state-of-the-art results. The proposed age estimation scheme also performs well in the cross-database age estimation task.