1. Age-invariant Face Recognition

2. Aging datasets
FG-NET
1,002 face images form 82 different people (2-69 years)

3. Aging datasets MORPH Album 1
1,690 face images from 515 different people (15-68 years)
Album2
94,000 face image from 24,000 different people (16-77 years)

4. Previous Work on Age-invariant Face Recognition
Generative solutions
Simulate the aging process and transform a facial image of one age to the target age.
Modeling age progression in young faces. In CVPR, 2006.
Age simulation for face recognition. In ICPR, 2006.
Age-invariant face recognition. PAMI, 2010.
A compositional and dynamic model for face aging. PAMI, 2010.
Challenges: The difficulties in modeling the complicated aging progress and in estimating the target age accurately.

5. Previous Work on Age-invariant Face Recognition
Discriminative solutions
Focus on image descriptor or classifiers which are robust against age progression.
Face verification across-age progression using discriminative methods. IEEE T-IFS, 2010
A discriminative model for age invariant face recognition. IEEE T-IFS, 2011.
Age Estimation and Face Verification Across Aging Using Landmarks, IEEE T-IFS, 2012
Local descriptors in application to the aging problem in face recognition. PR,2013.
Hidden factor analysis for age invariant face recognition. ICCV, 2013.
Cross-age reference coding for age-invariant face recognition and retrieval. ECCV, 2014.
Nonlinear Topological Component Analysis: Application to Age-Invariant Face Recognition, IEEE T-PAMI, 2015
A Maximum Entropy Feature Descriptor for Age Invariant Face Recognition, CVPR, 2015
Challenges: No single descriptor is versatile in discriminating faces with different age gaps.