Overview of research on facial ageing using the FG-NET ageing database

By Gabriel Panis, Andreas Lanitis, N. Tsapatsoulis, Tim Cootes. 2016

242 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Diversity in Faces (2019, methodology): “Many other data sets have been proposed for diﬀerent facial analysis tasks, such as age and gender classiﬁcation [24, 27, 31, 54–56], facial expression analysis [55], memorability [57], attributes [35] and aging [58].”
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks (2016, methodology): “1 Predicting the real and apparent age of a person.”
The impact of age-related variables on facial comparisons with images of children: algorithm and practitioner performance (2018, background): “Publicly available facial image databases that do contain images of children are restricted in terms of the: number of children, ages of the children, short age variations, and/or images of an uncontrolled nature (Panis et al., 2015; Ricanek et al., 2015; Ricanek & Tesafaye, 2006; Somanath, MV, & Ka”
Deep Face Recognition: A Survey (2018, methodology): “distractors to 1 million in the gallery set. However, an average of only 7 images per identity places restrictions on its intra-person variation, and it uses the subsets of FaceScrub [109] and FG-NET [112] to study the effects of pose and age variations. IJB-A [84] contains 5,397 images and 2,042 vi”
Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach (2017, background): “Due to limited space, we refer interested readers to reviews of the prior work on single facial attribute estimation in [10], [12], [14], [15], [39], [40], [41], [42].”