@InProceedings{8553281,
  author = {L. Yan and J. Chen},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Non-intrusive fingerprints extraction from hyperspectral imagery},
  year = {2018},
  pages = {1432-1436},
  abstract = {Fingerprint extraction plays an important role in criminal investigation and information security. Conventionally, latent fingerprints are not readily visible and imaging often requires to use intrusive manners. Hyperspectral imaging techniques provide a possibility to extract fingerprints in a non-intrusive manner, however it requires well-designed image analysis algorithms. In this paper, we consider the problem of fingerprint extraction from hyperspectral images and propose a processing scheme. The proposed scheme extracts image textures by local total variation (LTV) and uses Histogram of Oriented Gradient (HOG) information to fuse these channels. Experiment results with a real image show the ability of the proposed method for extracting fingerprints from complex backgrounds.},
  keywords = {feature extraction;fingerprint identification;hyperspectral imaging;image texture;nonintrusive fingerprints extraction;hyperspectral imagery;fingerprint extraction;criminal investigation;information security;hyperspectral imaging techniques;image analysis algorithms;image textures;histogram of oriented gradient information;HOG information;complex backgrounds;Hyperspectral imaging;Data mining;Feature extraction;Dimensionality reduction;Imaging;Principal component analysis;Fingerprint extraction;hyperspectral images;local total variation;texture;histogram of oriented gradient},
  doi = {10.23919/EUSIPCO.2018.8553281},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570439039.pdf},
}

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In this paper, we consider the problem of fingerprint extraction from hyperspectral images and propose a processing scheme. The proposed scheme extracts image textures by local total variation (LTV) and uses Histogram of Oriented Gradient (HOG) information to fuse these channels. Experiment results with a real image show the ability of the proposed method for extracting fingerprints from complex backgrounds.","keywords":"feature extraction;fingerprint identification;hyperspectral imaging;image texture;nonintrusive fingerprints extraction;hyperspectral imagery;fingerprint extraction;criminal investigation;information security;hyperspectral imaging techniques;image analysis algorithms;image textures;histogram of oriented gradient information;HOG information;complex backgrounds;Hyperspectral imaging;Data mining;Feature extraction;Dimensionality reduction;Imaging;Principal component analysis;Fingerprint extraction;hyperspectral images;local total variation;texture;histogram of oriented gradient","doi":"10.23919/EUSIPCO.2018.8553281","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570439039.pdf","bibtex":"@InProceedings{8553281,\n author = {L. Yan and J. Chen},\n booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},\n title = {Non-intrusive fingerprints extraction from hyperspectral imagery},\n year = {2018},\n pages = {1432-1436},\n abstract = {Fingerprint extraction plays an important role in criminal investigation and information security. Conventionally, latent fingerprints are not readily visible and imaging often requires to use intrusive manners. Hyperspectral imaging techniques provide a possibility to extract fingerprints in a non-intrusive manner, however it requires well-designed image analysis algorithms. In this paper, we consider the problem of fingerprint extraction from hyperspectral images and propose a processing scheme. The proposed scheme extracts image textures by local total variation (LTV) and uses Histogram of Oriented Gradient (HOG) information to fuse these channels. 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