Detection and Rectification of Distorted Fingerprints
Abstract of Detection and Rectification of Distorted Fingerprints
Detection and Rectification of Distorted Fingerprints.Elastic distortion of fingerprint is one amongst the foremost downside in fingerprint matching.
Since current fingerprint matching systems cannot match seriously distorted fingerprints, malicious persons could deliberately distort their fingerprints to cover their identity.
Existing distortion detection strategies need availableness of specialized hardware or fingerprint video, limiting their use in real applications.
In this paper, investigate a study on fingerprint distortion and rectification algorithm and use a dictionary based orientation field estimation approach to recognize latent fingerprints that is captured using ancient fingerprint sensing techniques.
In this wok to take advantage of stronger prior knowledge of fingerprints so as to further improve the performance. Promising results are obtained on 3 databases containing several distorted fingerprints, particularly NIST SD27 latent fingerprint database, FVC2004 DB1, and also the Tsinghua Distorted Fingerprint database.
Conclusion
Detection and Rectification of Distorted Fingerprints.
False non-match rates of fingerprint matchers are very high in the case of severely distorted fingerprints.
This generates a security hole in automatic fingerprint recognition systems which can be utilized by criminals and terrorists. For this reason, it is necessary to develop a fingerprint distortion detection and rectification algorithms to fill the hole.
This paper described a novel distorted fingerprint detection and rectification algorithm.
For distortion detection, the registered ridge orientation map and period map of a fingerprint are used as the feature vector and a SVM classifier is trained to classify the input fingerprint as distorted or normal.
