Fingerprint recognition using model-based density map.
Utilizing more information other than minutiae is much helpful for large-scale fingerprint recognition applications. In this paper, we proposed a polynomial model to approximate the density map of fingerprints and used the model's parameters as a novel kind of feature for fingerprint representation. Thus, the density information can be utilized into the matching stage with a low additional storage cost. A decision-level fusion scheme is further used to combine the density map matching with conventional minutiae-based matching and experimental results showed a much better performance than using single minutiae-based matching.
Wan
Dingrui
D
Department of Automation, Tsinghua University, Beijing, China. wandingrui00@mails.tsinghua.edu.cn
Zhou
Jie
J
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003878:Dermatoglyphics; D005385:Fingers / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012867:Skin / Q000033:anatomy & histology*; D016008:Statistical Distributions