MORPH: A Longitudinal Image Database of Normal Adult Age-Progression. 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.

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  1. Escalante-B., Alberto N.; Wiskott, Laurenz: Improved graph-based SFA: information preservation complements the slowness principle (2020)
  2. Antoniuk, Kostiantyn; Franc, Vojtěch; Hlaváč, Václav: V-shaped interval insensitive loss for ordinal classification (2016)
  3. Hernandez, John A. Ruiz; Crowley, James L.; Lux, Augustin; Pietikäinen, Matti: Histogram-tensorial Gaussian representations and its applications to facial analysis (2014) ioport
  4. Bereta, Michał; Karczmarek, Paweł; Pedrycz, Witold; Reformat, Marek: Local descriptors in application to the aging problem in face recognition (2013) ioport
  5. Rawls, Allen W.; Ricanek, Karl Jr.: MORPH: Development and optimization of a longitudinal age progression database (2009) ioport