Fingerprint matching algorithm for poor quality images

Abstract

The main aim of this study is to establish an efficient platform for fingerprint matching for low-quality images. Generally, fingerprint matching approaches use the minutiae points for authentication. However, it is not such a reliable authentication method for low-quality images. To overcome this problem, the current study proposes a fingerprint matching methodology based on normalised cross-correlation, which would improve the performance and reduce the miscalculations during authentication. It would decrease the computational complexities. The error rate of the proposed method is 5.4%, which is less than the two-dimensional (2D) dynamic programming (DP) error rate of 5.6%, while Lee's method produces 5.9% and the combined method has 6.1% error rate. Genuine accept rate at 1% false accept rate is 89.3% but at 0.1% value it is 96.7%, which is higher. The outcome of this study suggests that the proposed methodology has a low error rate with minimum computational effort as compared with existing methods such as Lee's method and 2D DP and the combined method.

Keywords

• fingerprint identification
• image matching
• dynamic programming
• fingerprint matching algorithm
• poor quality images
• low-quality images
• authentication method
• normalised cross-correlation
• computational complexities
• two-dimensional dynamic programming error rate
• 2D DP
• Lee method
• genuine accept rate
• false accept rate