Modified Hidden Factor Analysis for Cross-Age Face Recognition

Cross-age face recognition has remained a popular research topic because the sophisticated facial change across age disables regular face recognition systems. Widely applied in age-related tasks, the hidden factor analysis (HFA) model decomposes face feature into independent age and identity factors. However, the hypothesis that the identity and age factors are independent is not in accordance with the fact that aging has different appearance changes on different people's faces. To address this problem, this letter presents a novel method for cross-age face recognition, called age-identity modified HFA, which exploits a new latent factor modeled as a linear combination with the age factor and the identity factor. Hence, the cross-age identity information can be extracted and separated preferably. A maximum likelihood strategy is proposed to judge which gallery face has the same identity with the probe image, while we do not need to know what the probe identity is. Extensive experiments are performed on the benchmark aging datasets MORPH and FG-Net, and the recognition rate of our method outperforms HFA by 10.4% and 1.15%, respectively.