Age Induced Makeup (AIM)

Description

The Age Induced Makeup (AIM) dataset consists of presentation attacks in the form of age progressive makeups. The identities comprise of male and female subjects from various ethnicities. Professional artists have created varying degrees of facial makeups to generate an old-age appearance. The dataset has been created for experiments related to makeup-based presentation attacks on face recognition systems. The original (grayscale) version of the AIM dataset was released in 2019. This data was supplemented with RGB version in 2024.

The AIM dataset was originally curated to evaluate the vulnerability of face recognition systems against age-induced makeup attacks, followed by developing a detection mechanism (anti-spoofing related to makeups). The original version of dataset, released in 2019, and the corresponding publication, focused on a convolutional neural network (CNN) that required a single-channel (grayscale) version of the face images for face recognition tasks. To support the community in working with color data, the AIM dataset has been supplemented with an RGB version in 2024.

Details of the grayscale (original) Version

• Face Images: Aligned and cropped to 128 × 128 pixels.
• Channels: 1-channel (gray scale) images.

Details of the RGB Version

The extended version consists of:

• Face Images: Aligned and cropped to 112 × 112 pixels.
• Channels: 3-channel (RGB) images.
• Consistency: The RGB images correspond to the same frames/images from the original grayscale dataset.

Image Processing Details

• Face and Landmark Detection: Detected using MTCNN (Multi-task Cascaded Convolutional Network).
• Face Alignment: After cropping, the face regions are aligned using a 5-point transformation, as applied in the InsightFace repository.
• Image Format: For grayscale version, the images are stored as a 2D matrix HW (height, width). For RGB version, the images are stored in HWC format (height, width, channels), with the channel order being R-G-B.

We have made our best effort to ensure that RGB images (frames from video) align closely with the original grayscale images in terms of content. However, there may be slight mismatches in the exact frames due to variations in python software versions used to process the original video frames.

Reference

If you use this dataset, please cite the following publication: 

@article{Kotwal_TBIOM_2020,
author = {Kotwal, Ketan and Mostaani, Zohreh and Marcel, S\'{e}bastien},
title = {Detection of Age-Induced Makeup Attacks on Face Recognition Systems Using Multi-Layer Deep Features},
journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},
publisher = {{IEEE}},
year = {2020},
volume = {2}, 
number = {1},
month = {Jan},
pages = {15--25},
}