Face Spoofing Detection using Local Binary Pattern in MATLAB

Welcome to all
This video demonstrates the MATLAB implementation of “Face Spoofing Detection in Live Streaming”.

About the Problem:
Spoof may refer to Forgery of Goods or document. For scammers, spoofing is a tool for getting information or bypassing call blockers.

A Face Spoofing attack is an attempt to acquire someone else’s privileges or access rights by using a photo, video or a different substitute for an authorized person’s face. Some examples of attacks are as follows:
• Print attack: The attacker uses someone’s photo.
• Replay/video attack: This approach ensures behaviour and facial movements to look more ‘natural’ compared to holding someone’s photo.
• 3D mask attack: During this type of attack, a mask is used as the tool of choice for spoofing.

Spoofing Detection Approach
The most popular anti-spoofing state-of-the-art solutions include:
• Face liveness detection: A mechanism based on an analysis of how ‘alive’ a test face is. This is usually done by checking eye movement, such as blinking and face motion.
• Contextual information techniques: By investigating the surroundings of the image, we can try detecting if there was a digital device or photo paper in the scanned area.
• Texture analysis: Here small texture parts of the input image are probed in order to find patterns in spoofed and real images.
• User interaction: By asking the user to perform an action (turning head left/right, smiling, blinking eyes) the machine can detect if the action has been performed in a natural way which resembles human interaction.

About the Papers:
You may find many papers on Internet. I have used the following paper as reference:
“Face Spoofing Detection Using Colour Texture Analysis” by Zinelabidine Boulkenafet & Jukka Komulainen, University of Oulu

Algorithm used:
Training:
Step 1: Detect the Face Region in each frame.
Step 2: Crop and track the Face.
Step 3: Extract the LBP (Local Binary Pattern) features and save them.
Step 4: Train a SVM Classifier on saved features.
Testing: Do the first 3 steps of above and then classify the LBP Features of Test image as “Real” or “Fake”.

About the Database:
I created custom database of two types of Faces. One Real and another Fake Faces.
 
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[nbiresev]

How many images did you have in training set, did you train on images of your face and then showed this live demo using your face? Have you tried using paper mask, and cut place for eyes and nose, mouth?

[WasimHammoud]

Hello, I need your help to do the liveness check for my application, Can you?

[sgenovana]

please comment here if you find your code. I emailed you already.

[badarwasef]

Please sir can u provide the program used

[Tin-tuch-newsh]

would you mind send me the code if you can, thanks