- Force the length of features represents the age of this identity when taking this photo.
- In this case, after doing normalization, features are Age-Invariant.
- Aging process caused many problems (such as shape changes, texture changes, etc.), and those problems lead to large intra-class variation.
- In the field of AIFR, Conventional solutions model age feature and identity feature simultaneously. Considering the mixed feature are usually undecomposable, the property of mixing potentially reduce the robustness of recognizing cross-age faces.
- They proposed a new approach called OE-CNNs.
- Specifically, we decompose deep face features into two orthogonal components to represent age-related and identity-related features.
- In this way, identity-related features are then Age-Invariant.
- They also built a new dataset called Cross-Age Face dataset (CAF).
- This dataset includes about 313,986 face images from 4,668 identities. Each identity has approximately 80 face images.
- They manually washed the data. And this dataset is fairly randomly separated across ages.
4. Experiments & Results
Experiments on the MORPH Album 2 Dataset
Experiments on the LFW Dataset
Results on FG-NET Dataset and CACD-VS Dataset are also available in this paper.
5. Questions & Thoughts
- What if we embedding features to a flat instead of a sphere and let the height represents the age?
- Age can be replaced by other attributes of this image like pose, blur etc.
- The structure of their network can be adapted to GridFace.v7.relative_confidence_coefficient.
- The idea is actually quite similar to using Auto-Encoder to do the transfer.
- In comparison to A-Softmax, forcing the length of features to represents the age of this person actually restrict the capacity of this model which reduces the risk of over-fitting.