MSU Fingerprint Spoof Buster

A Python-based GUI application for fingerprint spoof detection, featuring the Spoof Buster algorithm by Tarang et. al (2018)

Project Repository

View on GitHub

This project was initially developed by Tarang et al (2018) and partially completed by me in 2022. My contribution focused on the final stages, where I developed a GUI to demonstrate the effectiveness of the algorithm visually. Mostly implemented the single mode and batch mode of operation.


Applications of a Fingerprint Spoof Detector

  • Access Control Systems: Enhances security in sensitive facilities by detecting spoofed fingerprints, ensuring only authorized personnel gain access.
  • Banking and Financial Services: Provides secure biometric authentication, reducing the risk of identity fraud in banking transactions.
  • Mobile Device Security: Protects user data on smartphones and tablets by preventing spoofed fingerprint access attempts.
  • Law Enforcement: Assists in verifying the authenticity of fingerprint evidence in forensic investigations.
  • Healthcare: Ensures secure access to patient information in medical facilities, safeguarding sensitive health data.


Key Features of the Fingerprint Spoof Buster

  • Capable of loading multiple models: Supports both CrossMatch_InceptionV3 and Limidigm_InceptionV3 models.
  • Minutiae detection, counting, and plotting: Detects, counts, and plots minutiae points for detailed fingerprint analysis.
  • Liveness detection scoring: Displays minutiae points and their scores in terms of liveness detection.
  • Flexible processing options: Supports batch processing as well as single-image processing.


The left case is a bonafide case where it has 56 minutiae points and all of them are live points. The right side case has only 36 detected cases all of which are PAs.

For more, please see the GitHub Page.