Abstract
Fingerprints are the most widely used form of human identification and verification due to their uniqueness and permanence. For that reason, many Automatic Fingerprint Identification Systems (AFIS) have been commercially produced and accepted by the international community. Though their performance is good, there is still room for improvement. One of the main concerns is poor fingerprint images that are caused by capturing devices. Thus, to improve the efficiency of AFIS, both image enhancement and feature extraction methods are required to be implemented. An effective feature extraction depends on the quality of its image whereby high image quality would normally produce genuine features. On the other hand, poor quality would lead to fake features that will result in false acceptance. This paper reviews several state-of-the-art methods of fingerprint image pre-processing including gray level normalization, noise removal and segmentation.
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