Age Recognition Using ResNet and Vision Transformer

Disclaimer: Here Age recognition refers to matching faces of the same person across different ages, NOT predicting a person's age given his/her face.

Dataset: Cross-Age Celebrity Dataset(CACD2000): Conatining celebrities faces across different ages.

The face crops and the identity mapping can be found in this link

Approach:

• Apply a face detctor to the input image to capture the face and preprocess it to the standard size.
• Initialize backbones with ImageNet pretrained weights
• Gernate training triplets in the following manner: anchor, positive(same id, different age), negative(different id, same age)
• Compute: a) Triplet loss and b) positive-negative cosine embedding loss
• Loss = triplet loss + learnable regularization factor * cosine embedding loss

TODOs:

• Dataset cleaning (face crop generation)
• Dataset preprocessing (anchor-positive-negative sampling)
• Model definitions
• Loss
• Preprocessor
• Model Training

Age recognizers using resnet101 and vit_b_16 backbones are now avaliable here.

Evaluation Statistics

Vit_b_16: Accuracy = 0.81 at threshold of 0.65 on the test dataset (test_vit_16_b.ipynb)

Resnet101: Accuracy = 0.76 at threshold of 0.65 on the test dataset (test_resnet.ipynb)

Sample Usage(?)

Using connan anime character as illustrative examples below (where the inspiration for the project is from)

sample.ipynb shows code samples to use the model for face comparison.