VADANA: A dense dataset for facial image analysis

Abstract

Analysis of face images has been the topic of in-depth research with wide spread applications. Face recognition, verification, age progression studies are some of the topics under study. In order to facilitate comparison and benchmarking of different approaches, various datasets have been released. For the specific topics of face verification with age progression, aging pattern extraction and age estimation, only two public datasets are currently available. The FGNET and MORPH datasets contain a large number of subjects, but only a few images are available for each subject. We present a new dataset, VADANA, which complements them by providing a large number of high quality digital images for each subject within and across ages (depth vs. breadth). It provides the largest number of intrapersonal pairs, essential for better training and testing. The images also offer a natural range of pose, expression and illumination variation. A parallel version with aligned faces is also created. Additionally, we provide relationships between subjects. We demonstrate the difference and difficulty of VADANA by testing with state-of-the-art algorithms. Our findings from experiments show how VADANA can aid further research on different types of verification algorithms. The variety of characteristics our data offers facilitate testing and benchmarking of other facial analysis algorithms.