MORPH: a longitudinal image database of normal adult age-progression

By Karl Ricanek Jr., Tamirat Tesafaye. 2006

1,290 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age (2019, background): “W ME E SE PPB [7] Gov. Ofﬁcial Proﬁles 1K **Skin color prediction MORPH [45] Public Data 55K merged no PubFig [31] Celebrity 13K Model generated predictions no IMDB-WIKI [46] IMDB, WIKI 500K no FotW [13] Flickr 25K yes CACD [10] celebrity 160K no DiF [39] Flickr 1M **Skin color prediction † CelebA…”
Diversity in Faces (2019, methodology): “The gender annotations used two class labels (male and female) and the age group labeling used seven classes ([0-3],[4-12],[13-19],[20-30],[31-45],[46-60],[61-]), as well as a continuous age value to be consistent with the automatic prediction labels.”
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace (2014, neutral): “MORPH [43].”
Deep Face Recognition: A Survey (2018, background): “g. 19). Cross-factor FR. Due to the complex nonlinear facial appearance, some variations will be caused by people themselves, such as cross-pose, cross-age, make-up. For example, CALFW [234], MORPH [135], CACD [24] and FG-NET [1] are commonly used datasets with different age range; CFP [145] only f”
Towards Measuring Fairness in AI: The Casual Conversations Dataset (2021, background): “Non-commercial MORPH dataset [27] is composed of longitudinal face images of people and provides binary gender labels come from government-furnished ID.”
Characterizing the Variability in Face Recognition Accuracy Relative to Race (2019, methodology): “El Khiyari and Wechsler [10] report results using a small subset of the MORPH [11] dataset, 362 subjects and 1448 images each for an African-American and a Caucasian cohort.”
Age-Invariant Face Recognition (2010, methodology): “There are two well-known public domain databases to evaluate facial aging models: FG-NET [23] and MORPH [24].”
Age and Gender Estimation of Unfiltered Faces (2014, background): “Another popular benchmark used by many in the past is the MORPH set [12], collected by the Face Aging Group at the University of North Carolina at Wilmington.”
Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition (2016, background): “DeepFace [33] reports that a deeply-learned face representation achieves the accuracy close to humanlevel performance on LFW dataset [11].”
Facial Age Estimation by Learning from Label Distributions (2010, neutral): “So, while his chronological age on the day is 25 , the age 26 can also be used to describe his facial appearance.”
Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach (2017, methodology): “years [15] on the MORPH II database [37].”
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition (2014, methodology): “LGXP 0.9369 0.0040 DCP 0.9557 0.0044 MsLBP 0.9494 0.0037 APPENDIX B PERFORMANCE OF DCP ON FACIAL AGING DATABASE We have also tested the performance of the face image descriptors on the Morph database [47]. The face images in Morph are geometrically and photometrically normalized as described in Sect”
Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset (2015, methodology): “By conducting extensive experiments, we show that the proposed method can outperform state-of-the-art methods on both MORPH [8] and CACD datasets.”
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks (2016, methodology): “Several public standard datasets [2, 42, 44] for real age estimation permit public performance comparison of the proposed methods.”
Neighborhood repulsed metric learning for kinship verification (2012, methodology): “Finally, we investigated the problem of self-kinship verification by using two widely used face aging databases, namely FG-NET [31] and MORPH [49] data sets.”