%0 Journal Article
%T Analysis of Facial Images across Age Progression by Humans
%A Jingting Zeng
%A Haibin Ling
%A Longin Jan Latecki
%A Shanon Fitzhugh
%A Guodong Guo
%J ISRN Machine Vision
%D 2012
%R 10.5402/2012/505974
%X The appearance of human faces can undergo large variations over aging progress. Analysis of facial image taken over age progression recently attracts increasing attentions in computer-vision community. Human abilities for such analysis are, however, less studied. In this paper, we conduct a thorough study of human ability on two tasks, face verification and age estimation, for facial images taken at different ages. Detailed and rigorous experimental analysis is provided, which helps understanding roles of different factors including age group, age gap, race, and gender. In addition, our study also leads to an interesting observation: for age estimation, photos from adults are more challenging than that from young people. We expect the study to provide a reference for machine-based solutions. 1. Introduction Human faces are important in revealing the personal characteristic and understanding visual data. The facial research has been studied over several decades in computer vision community [1, 2]. Analysis facial images across age progression recently attracts increasing research attention [3] because of its important real-life applications. For example, facial appearance predictor of missing people and ID photo automatic update system are playing important roles in simulating face aging of human beings. Age estimation can also be applied to age-restricted vending machine [4]. Most recent studies (see Section 2) of age-related facial image analysis mainly focus on three tasks: face verification, age estimation, and age effect simulation. In comparison, it remains unclear how humans perform on these tasks. In this paper, we study human ability on face verification and age estimation for face photos taken at across age progression. Such studies are important in that it not only provides a reference for future machine-based solutions, but also provides insight on how different factors (e.g., age gaps, gender, etc.) affect facial analysis algorithms. There are previous works on human performance for face recognition and age estimation; however, most of them are either focusing on nonage related issues such as lighting [5] or limited by the scale of image datasets (e.g., [6]). Taking advantage of the recent available MORPH dataset [7], which to the best of our knowledge is the largest publicly available face aging dataset, we are able to conduct thorough human studies on facial analysis tasks. For face verification, the task is to let a human subject decide whether two photos come from the same person (at different ages). In addition to report the general
%U http://www.hindawi.com/journals/isrn.machine.vision/2012/505974/