{
  "id": "1805.00911",
  "version": "v1",
  "published": "2018-05-02T17:16:18.000Z",
  "updated": "2018-05-02T17:16:18.000Z",
  "title": "Altered Fingerprints: Detection and Localization",
  "authors": [
    "Elham Tabassi",
    "Tarang Chugh",
    "Debayan Deb",
    "Anil K. Jain"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Fingerprint alteration, also referred to as obfuscation presentation attack, is to intentionally tamper or damage the real friction ridge patterns to avoid identification by an AFIS. This paper proposes a method for detection and localization of fingerprint alterations. Our main contributions are: (i) design and train CNN models on fingerprint images and minutiae-centered local patches in the image to detect and localize regions of fingerprint alterations, and (ii) train a Generative Adversarial Network (GAN) to synthesize altered fingerprints whose characteristics are similar to true altered fingerprints. A successfully trained GAN can alleviate the limited availability of altered fingerprint images for research. A database of 4,815 altered fingerprints from 270 subjects, and an equal number of rolled fingerprint images are used to train and test our models. The proposed approach achieves a True Detection Rate (TDR) of 99.24% at a False Detection Rate (FDR) of 2%, outperforming published results. The altered fingerprint detection and localization model and code, and the synthetically generated altered fingerprint dataset will be open-sourced.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-05-02T17:16:18.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "localization",
      "fingerprint images",
      "fingerprint alteration",
      "generated altered fingerprint dataset",
      "real friction ridge patterns"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}