CNN-for-face-anti-spoofing

All the face images listed below are in the dataset of CASIA-FASD

Description

This is my graduation project and it is based on " Deep Learning for Face Anti-Spoofing: An End-to-End Approach" published by Yasar Abbas Ur Rehamn, Lai Man Po and Mengyang Liu. Please check paper.

I built three CNN models Model A,B,C based on VGG-11. And it ultimately realized accuracies of 93.74%,91.15%,92.12% for identifying fake face image.

Configuration

Language: Python
Library: OpenCV
Platform: Tensorflow
Dataset: CASIA-FASD

Introduction

The face anti-spoofing is an technique that could prevent face-spoofing attack. For example, an intruder might use a photo of the legal user to "deceive" the face recognition system. Thus it is important to use the face anti-spoofint technique to enhance the security of the system.
The flow char of my work is as belows:

Prepare Dataset

CASIA-FASD datasets are consist of videos, each of which is made of 100 to 200 video frames. For each video, I captured 30 frames (with the same interval between each frame). Then, with the Haar_classifier, I was able to crop a person’s face from an image. These images make up training datasets and test datasets.

Training loss:

Model A:

Model B:

Model C:

Test:

Accuracy compared with the model in paper:

Test Example: