Proceedings ArticleDOI
MORPH: a longitudinal image database of normal adult age-progression
Karl Ricanek,Tamirat Tesafaye +1 more
- pp 341-345
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TLDR
The MORPH dataset as discussed by the authors is a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc.Abstract:Â
This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus for face recognition is highlighted in the evaluation of a standard face recognition algorithm, which illustrates the impact that age-progression, has on recognition rates. Assessment of the efficacy of this algorithm is evaluated against the variables of gender and racial origin. This work further concludes that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work.read more
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Age and gender classification using convolutional neural networks
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Automatic Age Estimation Based on Facial Aging Patterns
TL;DR: This paper proposes an automatic age estimation method named AGES (AGing pattErn Subspace), which is to model the aging pattern, which is defined as the sequence of a particular individual's face images sorted in time order, by constructing a representative subspace.
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Automatic age estimation based on facial aging patterns (vol 29, pg 2234, 2007)
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Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks
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Age Synthesis and Estimation via Faces: A Survey
TL;DR: The complete state-of-the-art techniques in the face image-based age synthesis and estimation topics are surveyed, including existing models, popular algorithms, system performances, technical difficulties, popular face aging databases, evaluation protocols, and promising future directions are provided.
References
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TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.
Proceedings ArticleDOI
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P.J. Phillips,Patrick J. Flynn,T. Scruggs,Kevin W. Bowyer,Jin Chang,K. Hoffman,J. Marques,Jaesik Min,William J. Worek +8 more
TL;DR: The face recognition grand challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images.
Proceedings ArticleDOI
The FERET evaluation methodology for face-recognition algorithms
TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.
Proceedings ArticleDOI
The CMU Pose, Illumination, and Expression (PIE) database
Terence Sim,Simon Baker,M. Bsat +2 more
TL;DR: Between October 2000 and December 2000, a database of over 40,000 facial images of 68 people was collected, using the CMU 3D Room to imaged each person across 13 different poses, under 43 different illumination conditions, and with four different expressions.
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