The micro-positive fingerprint identification algorithm MZFinger5.0 is the fingerprint identification algorithm of Guangzhou Weizheng Intelligent Technology Co., Ltd. with independent intellectual property rights. After years of market inspection, the algorithm is safe and reliable, with high recognition rate and good recognition of wet and dry fingers.
The micro-positive fingerprint identification algorithm MZFinger5.0 refers to a series of clear instructions for solving the problems such as fingerprint pre-processing, data feature extraction, feature matching, and fingerprint identification in the fingerprint recognition process. This paper summarizes the micro-fingerprinting algorithm MZFinger5.0 in three aspects: fingerprint image preprocessing, fingerprint image feature extraction and fingerprint matching.
First, fingerprint image preprocessing: In the process of fingerprint identification, the fingerprint image just acquired will be affected by factors such as noise, perspiration, and burrs, making the image picture unclear. The purpose of preprocessing is to improve the quality of the input fingerprint image to improve the features. The accuracy of the extraction. The pretreatment of fingerprint image in the entire fingerprint recognition system is similar to the function of the foundation for the whole house. The quality of the preprocessed image will affect the process of feature extraction and fingerprint matching. This is handled in the fingerprint recognition process. A good first step. Fingerprint image preprocessing is generally divided into four steps: image segmentation, image filtering, binarization and refinement.
1. Image segmentation. It mainly means that there is a mixture between the original fingerprint image obtained and the background area. It needs to be isolated from the two. This requires the initial processing of the image according to the size of the grayscale, and then normalization and segmentation to eliminate the background. area.
2. Image filtering. This is the most important step in the fingerprint image preprocessing process, mainly by denoising the noise-affected fingerprint image, and at the same time repairing and arranging the image, enhancing the ridge line structure contrast, and further obtaining a clearer image.
3. Binarization. After image filtering, the ridge portion is enhanced, but the intensity of the ridges is not exactly the same. This condition is mainly manifested in the difference in gray values. The binarization of an image means converting a grayscale image (gradation has 255 steps) into a binary image containing only black and white two grayscales, ie, two values ​​of 0 and 1. In this way, the gray values ​​of the ridges tend to be uniform, the image information is compressed, the storage space is saved, and the fingerprint feature extraction and matching are facilitated.
4. Refinement. It refers to the refinement of the fingerprints such as the trend and thickness of fingerprints after fingerprinting, which makes the fingerprint lines more smooth.
Second, the fingerprint image feature extraction: fingerprint image feature extraction algorithm there are many kinds, mainly based on grayscale image feature extraction, curve-based feature extraction, feature extraction based on singular points, based on ridge frequency feature extraction. Extracting feature points of fingerprint images can effectively reduce false feature points, extract accurate feature points, improve matching speed and fingerprint recognition performance, and reduce misrecognition rate and rejection rate of recognition systems.
Third, fingerprint matching: Fingerprint feature matching is mainly based on the matching of the detailed feature values. By comparing the input fingerprint detailed feature values ​​with the stored fingerprint detailed feature values, the fingerprint recognition is realized. When the two are compared, a critical value needs to be set to match. When the time is greater than this threshold, the fingerprint matches; when the match is less than the threshold, the fingerprint does not match. Feature matching is a key part of the identification system. The matching algorithm has a direct impact on the performance, speed, and efficiency of the recognition.
In the fingerprint recognition algorithm, from fingerprint input to matching, fingerprint image pre-processing, feature extraction, and fingerprint matching are three steps. This is the basic process that the fingerprint recognition algorithm needs to undergo. There is still every detail processing in each process. A lot of, this does not elaborate one by one, this text is only the general step that describes the micro-positive fingerprint identification algorithm MZFinger5.0.
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