PADify (pronounced as /ˈpædfaɪ/) is a project to perform presentation attack detection (PAD) on publicly available datasets by using different intrinsic image properties along with Convolutional Neural Networks.

Presentation Attack Detection

_{Fig 1 - Examples of Presentation Attack Detection}

Pipeline Overview

_{Fig 2 - Overview of proposed method}

Properties

• Illuminant Maps (Carvalho et al.)
• Saliency (Zhu et al.)
• Depth (Godard et al.)

Datasets

• CASIA Face Anti-Spoofing Database
• Replay Attack
• NUAA Imposter Database
• ROSE-Youtu Face Liveness Detection Dataset (In Progress)

License

This file is licensed under the MIT license. You can check the LICENSE file for more information.