A new approach for classification of fingerprint image quality

Fingerprint identification is a technology which has been widely accepted for personal identification in many areas such as criminal investigation, access control, and Internet authentication due to its uniqueness. Most available systems for fingerprint identification use the minutiae matching for identification. The performance of minutiae extraction algorithms relies heavily on the quality of the fingerprint image because the minutiae-based approach is very sensitive to noise or image-quality degradation. Poor-quality images result in spurious and missing features which degrades the performance of the identification system. So if the quality of the image can be examined first, the image with very poor quality can be rejected. The useful information can be combined into the procedure of post-processing and matching scheme to improve the identification process. Therefore it is desirable to design a classification scheme which is able to examine the quality of a fingerprint image before it is processed by the fingerprint identification systems. Decision-tree is one of the well-known data mining methods that are used in classification problems because of its fast and effective features. In this paper a new classifier based on decision tree theory for classification of fingerprint image quality is proposed. This new classifier has many advantages in solving the fingerprint quality classification problem. It can generate the rules with almost all of the original information from the classifier even after all of the original fingerprint images are lost. It means it will be not necessary to save all the original fingerprint images because the classifier can combine the rules with the new coming data to build a more precise classifier with more information to classify the fingerprint image quality. And the new classifier can give weight to different fingerprint images if they come from the sensors with different importance. The advantages of the new classifier make the fingerprint image classification system very powerful and feasible. The proposed method has a very good performance which is proved by the experiments.