With the popularity of mobile payment services, fingerprint recognition technology has become the standard of today's smart phones, and the new technology such as CMOS image sensor / TFT display, ultrasonic detection, etc., has further ushered in the development of its market. New Year.
According to research firm Yole, the compound annual growth rate (CAGR) of the fingerprint identification market will reach 19% in the next five years, and the market size is expected to increase from 2.8 billion US dollars in 2016 to 4.7 billion US dollars in 2022.
Initially, it was only used as a component to facilitate the unlocking function of mobile phones. Fingerprint recognition sensors, now driven by the mobile payment service of smart phones, have become an important security element for mobile payment. According to industry analysts, most of the current fingerprint identification market comes from the OEM's demand for all-glass design and waterproof function. This has prompted new technologies such as CMOS/TFT and ultrasonic detection to further promote the evolution of highly integrated fingerprint recognition technology.
According to statistics, the shipment of fingerprint recognition sensors in 2016 has reached 689 million, compared with 23 million in 2013, CAGR reached 210%. Of course, a large number of demand has also led to a lower average price of fingerprint recognition sensors, which has now fallen from $5 to $3, or even lower, and suppliers will continue to face price pressure in the future.
The fingerprint identification market is highly flexible for sensor manufacturers. Although the market scale is very impressive in the next five years, how to stand out in the fierce market competition is still a difficult problem for the relevant manufacturers.
Second, the principle of fingerprint recognitionFingerprint recognition technology includes the following two main identification technologies:
The first is a method of statistical comparison using different fingerprint images;
The second method is a method of comparing the feature information inherent in the fingerprint image itself.
The first method mainly compares the two fingerprint images statistically to see the similarity between them, and judges whether the two fingerprints are taken from the same person according to the size, thereby realizing the identity recognition function. The second method is to compare their feature information and confirm their identity based on the structural features of the two fingerprint images. Features contain two types: global feature types and local feature types.
The whole process of fingerprint identification technology is:(1) Using a fingerprint collection device to collect fingerprint images.
(2) Preprocessing a large number of noise points in the fingerprint image, thereby improving the efficiency of subsequent processing. After pre-processing, an outline of the fingerprint image is obtained to prepare for the next feature extraction.
(3) Perform feature extraction of the fingerprint image and extract its feature information points.
(4) Feature matching is performed on the fingerprint image, and the extracted feature points are compared with the feature points pre-stored in the database, and the identity is determined by comparison. According to a study by British scholar ERHerry, if two pairs of logarithms of feature points are coincident in two fingerprint images, they can be considered to be taken from the same person.
The main performance parameters of the fingerprint identification system are as follows:
(1) Misrecognition rate: refers to the probability that two different fingerprints are incorrectly recognized as the same fingerprint;
(2) Rejection rate: refers to the fact that two different fingerprint samples of the same finger cannot match, that is, the probability of being considered to be from different fingers;
(3) Equal error rate: the value when the first and second errors are equal;
(4) Registration time: the time required from the fingerprint collection to the completion of the fingerprint feature submission;
(5) Matching time: the time required for two fingerprint samples to perform a comparison match;
(6) Size of the template feature: the storage capacity of the fingerprint feature extracted from a fingerprint image;
(7) Size of allocated memory: The amount of memory that the computer system needs to occupy at various stages of fingerprint recognition.
Third, the quality evaluation of fingerprint imagesAfter the image is captured into the system by the fingerprint acquisition device, we need to evaluate the quality of the captured fingerprint image. If the quality of the image is not up to standard, it will have an impact on the later stage. Therefore, the fingerprint image needs to be evaluated. Currently, there are several methods for evaluating the quality of fingerprint images:
(1) Calculate the signal to noise ratio of the image:
This method refers to finding the ratio of the signal to the variance of the noise. First, the local variance of all the pixels of the image is calculated. The maximum value of the local variance is set as the signal variance, and the minimum value is set as the noise variance. The ratio of these is determined, and then converted into a dB number. Finally, the empirical formula is used to correct. This method performs generally in terms of efficiency.
(2) Count the number of detail points of the fingerprint image:
Identify and count the number of minutiae points in the fingerprint image. It is judged by the number of the number whether the quality of the fingerprint image is within the acceptable range. This method is theoretically feasible, but it is not efficient because it first needs to preprocess the fingerprint and extract the minutiae.
(3) Visual visitor observation:
The method is based on the visual evaluation process and the guest observation degree, and uses the set evaluation parameters to evaluate a comprehensive result of the quality of the fingerprint image. This method can make a good judgment on the quality of the fingerprint image from the whole. However, from a local point of view, the texture analysis of the fingerprint lacks the judgment of the fingerprint direction information.
(4) Calculate the fingerprint image direction information:
Starting from the local features of the fingerprint image, the quality of the fingerprint image is determined by combining the global features of the fingerprint. Whether the image is acceptable is determined by detecting the effective area and sharpness of the image. The specific method is: first, determining the foreground block and the background block by calculating image direction information; then, by comparing the ratio of the foreground block and the background block, determining whether it is a partial finger; again, judging by the size of the contrast of the image block is Fingers or wet fingers (dry fingers have a higher contrast and wet fingers have a lower contrast).
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