Face verification across age progression

By Narayanan Ramanathan, Ramalingam Chellappa. Computer Vision and Pattern Recognition, 2005

342 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3342018e8defb402896d2133cda0417e49f1e9aa

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Citations in the corpus

Multi-task warped Gaussian process for personalized age estimation (2010, methodology): “Ramanathan and Chellappa [23] used a craniofacial growth model to synthesize facial images.”
Age Synthesis and Estimation via Faces: A Survey (2010, background): “They also investigated the face similarity measurement across age progression and face verification across age progression by Bayesian age difference classifiers (intrapersonal and extra-personal spaces built on age difference images from the passport images) [23].”
Neighborhood repulsed metric learning for kinship verification (2012, methodology): “Bayesian eigenfaces [48]: Face images were represented by the eigenspace features and the Bayesian classifier was combined to model the intrapersonal and interpersonal face differences.”
Automatic Age Estimation Based on Facial Aging Patterns (2007, methodology): “For example, Ramanathan and Chellappa [18] proposed a method for face verification across age based on a Bayesian classifier.”
Age-Invariant Face Recognition (2010, background): “Studies on face verification across age progression [9] have shown that: 1) Simulation of shape and texture variations caused by aging is a challenging task as factors like lifestyle and environment also contribute to facial changes in addition to biological factors, 2) the aging effects can be…”
Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval (2014, background): “Most existing age-related works for face image analysis focus on age estimation [14,15,40,25,34,7,38,6,10,22,21] and age simulation [16,30,28,29,24].”
Modeling Age Progression in Young Faces (2006, background): “Ramanathan and Chellappa [20] proposed a Bayesian age-difference classifier built on a probabilistic eigenspaces framework to perform face verification across age progression.”
Human age estimation using bio-inspired features (2009, background): “Thus face aging is uncontrollable and personalized [24] [7] [11].”
Learning from facial aging patterns for automatic age estimation (2006, methodology): “For example, Ramanathan and Chellappa [21] proposed a method for face veri.cation across age progression based on Bayesian classi.er, which is able to roughly estimate the age di.erence between a pair of face images of the same individual.”
Human Age Estimation With Regression on Discriminative Aging Manifold (2008, methodology): “Based on different application protocols, it can be taken as the multiclass classification or regression problems; 2) large size aging database is often hard to collect, especially the chronometrical image series for a single person; 3) as mentioned in [9], [15], the age progression displayed on fac”