Recent studies in patch-based Gaussian Mixture Model (GMM) approaches for face age estimation present promising results. We propose using a hidden Markov model (HMM) supervector to represent face image patches, to improve from the previous GMM supervector approach by capturing the spatial structure of human faces and loosening the assumption of identical face patch distribution within a face image. The Euclidean distance of HMM supervectors constructed from two face images measures the similarity of the human faces, derived from the approximated Kullback-Leibler divergence between the joint distributions of patches with implicit unsupervised alignment of different regions in two human faces. The proposed HMM supervector approach compares favorably with the GMM supervector approach in face age estimation on a large face dataset.
['Xiaodan Zhuang', 'X. Zhou', 'Mark Hasegawa-Johnson', 'Thomas S. Huang']
Face age estimation using patch-based hidden Markov model supervectors