Fingerprint Image Preprocessing Research Articles

A Review of Algorithms for Fingerprint Image Acquisition, Preprocessing and Minutiae Extraction

Biometric recognition distinguishes between individuals using physical, chemical or behavioral attributes of the person. These attributes are called biometric identifiers or traits, and include fingerprint, palmprint, iris, face, voice, signature, gaint, and DNA among others. Fingerprint recognition is one of the oldest and most reliable biometric used for personal identification. Fingerprint has come a long way from tedious manual fingerprint matching. The ancient procedure of matching fingerprints manually was extremely cumbersome and time-consuming and required skilled personnel. In this paper, a review of algorithms for the various stages involved in fingerprint recognition such as fingerprint image acquisition, segmentation, normalization, ridge orientation estimation, ridge frequency, Gabor filtering, binarization, thinning, minutiae extraction, template generation, and template matching is presented. It was established that minutiae features of a person fingerprint truly make fingerprint of individual to be unique.

A Review of Fingerprint Image Pre-processing

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.

Fingerprint Segmentation Algorithms: A Literature Review

Fingerprint image segmentation is part of pre-processing in fingerprint image recognition system. It has a critical effect to the fingerprint image recognition system. This paper review the different algorithms particularly used for fingerprint segmentation. Adaptive Total Variation Model, Directional Total Variation Model, method based on combination of ridge orientation and frequency features, method based on orientation field information combined with statistical characteristics of gray, Ridge Template Correlation, method using three pixel features(being the coherence, the mean and the variance) are discussed and compared based on their performance and concluded that Adaptive Total Variation Model and Directional Total Variation Model provides better results, so it will be good for the field of researches. General Terms Image recognition, fingerprint image preprocessing, algorithms of fingerprint segmentation.

Fingerprint Preprocessing and Recognition System Development and Design

fingerprint image preprocessing and is one of the branch of image processing and pattern recognition, after several years of development the increasing maturity of the technology. Due to the uniqueness and invariability of fingerprints, and the feasibility and practicability of the fingerprint identification technology, fingerprint identification has become the most popular, the most convenient, one of the most reliable personal identity authentication technology. Although on this technology has a variety of molding products, but because many of the core technology by commercial interests and confidentiality need without open, as well as the development of the society put forward higher requirements on the performance of the system, so in this field of research, still has important theoretical significance and practical value.

A New Gradual Segmentation Algorithm of Fingerprint Image

Segmentation is an important part of fingerprint image preprocessing. Effective segmentation not only reduces the time of subsequent processing but also improves the reliability of feature extraction considerably. After introducing segmentation, we suggest improved mean and variance-orientation coherence gradual segmentation algorithm of fingerprint image. Morphology has been applied as preprocessing to reduce the number of classification errors. The algorithm is tested on FVC2002 database, only 0.79% of the blocks are misclassified, while the preprocessing further reduces this ratio. Experimental results verify the feasibility of this algorithm.

An Approach to Fingerprint Image Pre-Processing

In this paper we have used all existing algorithms. When a fingerprint image is captured it is made pass through all the algorithms arranged in a particular order. We found that if we process a fingerprint in this particular order, the final output is good enough for minutiae detection and feature extraction. We have done many experiments on fingerprint images and found that this particular order of processing is producing better result. But for this we assume that the quality of the captured image is good enough. We have not worked on image quality enhancement. So if the input image is good our method will produce a good output. Off course this is a limitation of our proposed method, but if image is captured using a good quality device, then our method will produce an equal quality output as in other existing techniques.

The Design of Fingerprint Identification System Based on TMS320VC5402

Due to the unique and lifelong invariance characteristics of the fingerprint, this paper presents a fast fingerprint identification system by FPS200 solid-state fingerprint sensors and TMS320VC5402 DSP chip.In the system, FPS200 completed fingerprint collection, DSP chip to complete the fingerprint image pre processing of data; combined with the characteristics of the two chips, given the system hardware design, software design flow. The results showed that: the system is well designed, low cost, real-time and high reliability, with important practical value and broad application prospects.

Design of a Wireless Fingerprint Authentication Service Platform Using the SOPC Technology

Fingerprint authentication is becoming an important security requirement in various embedded systems. While traditional identification method, user ID plus password cannot satisfy users’ needs due to various defects, they are increasingly being replaced by biometric technologies such as fingerprint, face, and voice recognition, which are known to provide higher levels of security for user authentication. In this paper, an effective wireless fingerprint authentication system is presented. We designed the wireless fingerprint authentication based on Altera's Nios II processor. The whole system design includes real-time fingerprint image collection, image preprocessing, feature extraction, matching processes, a database and wireless transmission.

Fingerprint Segmentation Based on Fractal Dimension

Fingerprint segmentation is an important problem in fingerprint image preprocessing. This paper describes a new approach to the segmentation of fingerprint images based on fractal dimension. First, the Sobel operator is used to calculate the gradient of fingerprint image, and then we employ the concept of fractal dimension to further analyze the image produced by the first step. By estimating the fractal dimension of the foreground and the background of the fingerprint, and combining grayscale as features, finally accomplish the segmentation of fingerprint. The experimental results show that the proposed method performs well in fingerprint segmenting and is better than the existing method.