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Year : 2017  |  Volume : 3  |  Issue : 2  |  Page : 90-96

Research and realization of ten-print data quality control techniques for imperial scale automated fingerprint identification system

1 Division of Forensic Science, Liaoning Provincial Police, Shenyang, People's Republic of China
2 Beijing Oriental Golden Finger Technology Company Limited, Beijing, People's Republic of China
3 School of Mathematical Sciences, University of Chinese Academy of Sciences; Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, People's Republic of China

Correspondence Address:
Tong Zhao
School of Mathematical Sciences, University of Chinese Academy of Sciences, 19A Yuquan Road, Shijingshan, Beijing 100049
People's Republic of China
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jfsm.jfsm_49_17

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As the first individualization-information processing equipment put into practical service worldwide, Automated Fingerprint Identification System (AFIS) has always been regarded as the first choice in individualization of criminal suspects or those who died in mass disasters. By integrating data within the existing regional large-scale AFIS database, many countries are constructing an ultra large state-of-the-art AFIS (or Imperial Scale AFIS) system. Therefore, it is very important to develop a series of ten-print data quality controlling process for this system of this type, which would insure a substantial matching efficiency, as the pouring data come into this imperial scale being. As the image quality of ten-print data is closely relevant to AFIS matching proficiency, a lot of police departments have allocated huge amount of human and financial resources over this issue by carrying out manual verification works for years. Unfortunately, quality control method above is always proved to be inadequate because it is an astronomical task involved, in which it has always been problematic and less affiant for potential errors. Hence, we will implement quality control in the above procedure with supplementary-acquisition effect caused by the delay of feedback instructions sent from the human verification teams. In this article, a series of fingerprint image quality supervising techniques has been put forward, which makes it possible for computer programs to supervise the ten-print image quality in real-time and more accurate manner as substitute for traditional manual verifications. Besides its prominent advantages in the human and financial expenditures, it has also been proved to obviously improve the image quality of the AFIS ten-print database, which leads up to a dramatic improvement in the AFIS-matching accuracy as well.

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