Based on multiple influencing factors, a new method of automatic fingerprint image quality evaluation is proposed for improving the success rate of automation fingerprint identification system (AFIS). At first, the original image’s local texture, global texture, available size and dry or wet condition are regarded as quality impact factors, local texture quality score is calculated by local gradient correlation, and then the last three factors’ quality scores are obtained by block computation thought. Then, with different influence weights, the above four impact factors are linked together to assess image quality synthetically. Finally, effect of partial impact factor is adjusted to correct the final quality score. FVC2004DB2_B is used for algorithm testing. The results suggest that this method can reasonably classify fingerprint image into 5 grades and the precision can achieve 97.5%, and that shows the method is helpful to success rate of AFIS.
