A Novel Fingerprint Image Enhancement based on Super Resolution

Fingerprint is a most common and broadly accepted biometric trait used for personal authentication. In fingerprint-based authentication, the feature extraction module extract features, and these extracted characteristics are used for authentication. In fingerprints, the feature extraction module heavily depends on the status of the image. However, in practice, always getting a good quality fingerprint image is not possible. Moreover, a notable number of fingerprints collected are of poor quality. The accurate extraction of fingerprint characteristics from a lesser quality fingerprint image is a challenging problem. Fingerprint enhancement is introduced to resolve this issue. Hence in this paper, we introduce a fingerprint enhancement technique using a Deep Convolution Neural Network (DCNN), which improves image quality. The proposed method consists of super-resolution, followed by filtering and enhancement. The proposed method provides better results as compared with the conventional fingerprint enhancement methods. The experimental results determine that the proposed strategy improves the visual clarity of low-quality images and reduces the error rates during the fingerprint matching.