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

TLDR

MORPH is a longitudinal face database designed to support research in adult age‑progression, including face modeling, photo‑realistic animation, and face recognition. The study evaluates a standard face‑recognition algorithm on MORPH, comparing performance across gender and racial groups. The database, the largest publicly available longitudinal face set spanning up to twenty years and including key physical parameters, demonstrates that age‑progression significantly degrades recognition rates, a problem observed across multiple algorithms.

Abstract

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.