This paper demonstrates that, for large-scale tests, the match and non-match similarity scores have no specific underlying distribution function. The forms of these distribution functions require a nonparametric approach for the analysis of the fingerprint
{
"title_en": "Rich Transcription Website",
"designation": "",
"series": "",
"status": "",
"date": "2004-12-01",
"date_original": "December 1, 2004",
"url": "https://www.nist.gov/publications/rich-transcription-website",
"source": "NIST Publications",
"display_en": "",
"authors_en": "John S. Garofolo",
"abstract_excerpt_en": "This paper demonstrates that, for large-scale tests, the match and non-match similarity scores have no specific underlying distribution function. The forms of these distribution functions require a nonparametric approach for the analysis of the fingerprint",
"id": "298a75577dba57a3b7c9",
"sources": [
"NIST Publications"
],
"source_records": [
{
"title_en": "Rich Transcription Website",
"designation": "",
"series": "",
"status": "",
"date": "2004-12-01",
"date_original": "December 1, 2004",
"url": "https://www.nist.gov/publications/rich-transcription-website",
"source": "NIST Publications",
"display_en": "",
"authors_en": "John S. Garofolo",
"abstract_excerpt_en": "This paper demonstrates that, for large-scale tests, the match and non-match similarity scores have no specific underlying distribution function. The forms of these distribution functions require a nonparametric approach for the analysis of the fingerprint"
}
],
"translation_state": "none"
}