AIM (Age Induced Makeup)

The Age Induced Makeup (AIM) dataset consists of 456 short video recordings of both bona fide and presentation attacks (PA) from 72 subjects. The obfuscation attacks have been created from age progressive makeups on male and female subjects.

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Dataset 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 detection of makeup-based presentation attacks on face recognition systems.


Samples of age-induced makeup with different levels of makeup intensity from AIM dataset. For each row, the left image is bona-fide, and intensity of makeup increases from left to right.


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

	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 = {2019},

Data Collection:

The AIM dataset consists of 456 video recordings from both bona fide and presentation attack (PA) videos (each ≈ 10 s in duration). These videos have been acquired in using the RGB channel of Intel RealSense SR300 camera.

The dataset consists of 240 bona fide (non-makeup) presentations corresponding to 72 subjects; and 216 attack (age induced makeup) presentations captured from a subset of 20 subjects. For every participant of makeup presentation of AIM, a bona fide (non-makeup) video is also available.

Makeups were created by professionals using regular makeup materials to compose age-inducing effects like coloring of eyebrows, and creation of wrinkles on cheeks, or forehead. No prosthetic objects or materials were considered.

The AIM dataset is a subset of WMCA dataset collected at Idiap Research Institute. For details on WMCA dataset, please refer:

A complete preprocessed data for the aforementioned videos have been provided to facilitate reproducing experiments from the reference publication, as well as to conduct new experiments. The details of preprocessing can be found in the reference publication.

The implementation of all experiments described in the reference publication is available at

Experimental Protocol:

The reference publication considers the experimental protocol named grandtest. For a frame-level evaluation, 20 frames from each video have been used. For the grandtest protocol, videos were divided into fixed, disjoint groups: train, dev, and eval. Each group consists of unique subset of subjects. (Subjects of one group are not present in other two).

Details of the grandtest protocol are summarized below:

Partition #Videos # Frames Split Ratio (%) Total Frames
train bona fide 86 1720 54.43 3160
train PA 72 1440 45.56
dev bona fide 80 1600 52.63 3040
dev PA 72 1440 47.37
eval bona fide 74 1480 50.68 2920
eval PA 72 1440 49.32
Total 456 9120   9120