Modeling Age Progression in Young Faces

By Narayanan Ramanathan, Ramalingam Chellappa. 2006

449 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

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Citations in the corpus

Diversity in Faces (2019, methodology): “The third, coding scheme 3, provides a set of craniofacial ratios from [4].”
Age Progression/Regression by Conditional Adversarial Autoencoder (2017, methodology): “For example, the physical model-based methods [28, 27, 15, 22] parametrically model biological facial change with age, e.”
Automatic Age Estimation Based on Facial Aging Patterns (2007, background): “For example, Ramanathan and Chellappa [18] proposed a method for face verification across age based on a Bayesian classifier.”
Age-Invariant Face Recognition (2010, methodology): “A few seminal studies [3], [13] have demonstrated the feasibility of improving face recognition accuracy by simulated aging.”
AgeDB: The First Manually Collected, In-the-Wild Age Database (2017, methodology): “To this end, we compare the performance of RJIVE with the Illumination Aware Age Progression (IAAP) method [19], Coupled Dictionary Learning (CDL) method [45], Deep Aging with Restricted Boltzmann Machines (DARB) method [32], CG [36], and Recurrent Face Aging (RFA) method [49].”
How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis (2020, methodology): “Face morphology is also relevant for attributes such as age and gender [135].”
Human Age Estimation With Regression on Discriminative Aging Manifold (2008, background): “Apart from a large body of research on aging synthesis and rendering [1], [10], however, there are relatively few publications specifically on age estimation.”
Age and Gender Estimation of Unfiltered Faces (2014, methodology): “More recently, [18] presented a similar approach for modeling the age of young people.”
Age Synthesis and Estimation via Faces: A Survey (2010, methodology): “There are two reasons that people do not use the mathematical formulation for age estimation: 1) The mathematical model cannot characterize the head profile in a natural way, especially when the ages are close to adults [53], and 2) the head profile is difficult to measure from 2D face images.”
Age and gender classification using convolutional neural networks (2015, methodology): “More recently, [41] uses a similar approach to model age progression in subjects under 18 years old.”
Demographic Estimation from Face Images: Human vs. Machine Performance (2015, methodology): “The anthropometry-based approach adopted in [13], [14], [22], [36], utilizes the distance ratios between individual facial landmarks to describe the topological and configural differences among face shapes across different demographic groups.”
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks (2016, unknown): “We refer to [42, 20, 14, 26, 2, 9] for broader literature reviews.”
What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics (2016, methodology): “From this category of facial features, age can be quantified from features such as the craniofacial index (see Ramanathan and Chellappa [132]), wrinkles, skin quality [133], facial hair and chin line (Alberta et al.”
Multi-task warped Gaussian process for personalized age estimation (2010, methodology): “Ramanathan and Chellappa [23] used a craniofacial growth model to synthesize facial images.”