Title :
Hidden Factor Analysis for Age Invariant Face Recognition
Author :
Dihong Gong ; Zhifeng Li ; Dahua Lin ; Jianzhuang Liu ; Xiaoou Tang
Author_Institution :
Shenzhen Key Lab. of Comput. Vision & Pattern Recognition, Shenzhen Inst. of Adv. Technol., Shenzhen, China
Abstract :
Age invariant face recognition has received increasing attention due to its great potential in real world applications. In spite of the great progress in face recognition techniques, reliably recognizing faces across ages remains a difficult task. The facial appearance of a person changes substantially over time, resulting in significant intra-class variations. Hence, the key to tackle this problem is to separate the variation caused by aging from the person-specific features that are stable. Specifically, we propose a new method, called Hidden Factor Analysis (HFA). This method captures the intuition above through a probabilistic model with two latent factors: an identity factor that is age-invariant and an age factor affected by the aging process. Then, the observed appearance can be modeled as a combination of the components generated based on these factors. We also develop a learning algorithm that jointly estimates the latent factors and the model parameters using an EM procedure. Extensive experiments on two well-known public domain face aging datasets: MORPH (the largest public face aging database) and FGNET, clearly show that the proposed method achieves notable improvement over state-of-the-art algorithms.
Keywords :
expectation-maximisation algorithm; face recognition; learning (artificial intelligence); probability; statistical analysis; EM procedure; FGNET dataset; HFA; MORPH dataset; age factor; age invariant face recognition; expectation-maximisation algorithm; facial appearance; hidden factor analysis; identity factor; intra-class variations; learning algorithm; probabilistic model; public domain face aging datasets; Aging; Computational modeling; Face; Face recognition; Mathematical model; Training; Vectors; Age invariance; face recognition;
Conference_Titel :
Computer Vision (ICCV), 2013 IEEE International Conference on
Conference_Location :
Sydney, NSW
DOI :
10.1109/ICCV.2013.357