Fingerprint-based identification is one of the most well-known and publicized biometrics for personal identification. It remains a reliable, efficient and commonly accepted biometric. In this paper, a fingerprint recognition system for identifying the low quality fingerprint images on Myanmar National Registration Cards (NRCs) is developed. Traditional minutia based approach is not robust to poor quality fingerprint images. In proposed system, ridge feature-based approach for fingerprint recognition using contextual filter and single pass thinning algorithm is developed. The input image is preprocessed and gabor filtering is applied for ridge line enhancement. The system extracts the ridge line features from the skeleton image derived from single pass thinning algorithm and it is compared to the database using Euclidean distance metric. The effectiveness of the proposed system can be confirmed through the experimental results.
Fingerprint-based identification is one of the most well-known and publicized biometrics for personal identification. Extracting features out of poor quality prints is the most challenging problem faced in this area. In this paper, the texture feature based approach for fingerprint recognition using Discrete Wavelet Transform (DWT) is developed to identify the low quality fingerprint from inked-printed images on paper. The fingerprint image from paper is very poor quality image and sometimes it is complex with fabric background. Firstly, a center point area of the fingerprint is detected and keeping the Core Point as center point, the image of size w x w is cropped. Gabor filtering is applied for fingerprint enhancement over the orientation image. Finally, the texture features are extracted by analyzing the fingerprint with Discrete Wavelet Transform (DWT) and Euclidean distance metric is used as similarity measure. The accuracy is improved up to 98.98%.
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