What is the principle of iris unlocking? Intelligent iris lock working principle is introduced
What is the principle of iris unlocking? Intelligent iris lock working principle introduction. Iris lock is an emerging type of smart lock in recent years. The security of this technology is higher than that of fingerprint lock. It also requires higher technology to support it. I believe that many enthusiasts do not know the principle of unlocking the iris. In fact, this is also a kind of biometric technology. In the previous years, the concept of iris smart lock was proposed. In recent years, iris smart lock and iris intelligent identification access control have emerged. The method of iris recognition was first proposed by the American ophthalmologists Leonard Flom and Arin Safir in 1987. The iris recognition algorithm was developed by Dr. John Dargman of the University of Cambridge. He proposed a mathematical algorithm for encoding and comparing irises. The iris is a ring-shaped color structure around the pupil. It has a rich and varied texture pattern that forms the basis for iris recognition. The iris recognition technology scans and images the iris pattern through a light that approximates infrared rays, and determines the degree of similarity through the XOR operation of the pixel positions in the pattern. The iris recognition process first needs to separate the iris from the eye image, and then perform feature analysis. Theoretically, the probability of finding two identical irises is 1.2 million. This is also the most accurate of all currently known biometric technologies. Iris recognition has not been widely used in the civilian market because of complex equipment, short scanning distances (generally within 7 inches), and users' psychological concerns. A person's iris remains unchanged throughout its development and has a high degree of uniqueness. Therefore, it is a kind of safe (close) amount of human biological characteristics. The iris is a different concept from the retina. It exists on the surface of the eye (the lower part of the cornea). It is a colored annular film around the pupil. The color of the human eye is determined by the iris and is not affected by the internal diseases of the eyeball. The iris reading device is mainly a video camera, and information reading can be completed as long as the eye is facing the camera. Its characteristic is that it does not need to touch the reading equipment, but it also needs human body cooperation (can not close your eyes, side to the camera) in order to ingest useful information, because it is inconvenient to strictly regulate the location of the person, the system's misrecognition rate is very low, rejection rate is Higher. Another advantage of the iris as a feature is that it is not easy to counterfeit, but the environmental conditions are not yet widely used in security systems. The general process of iris recognition technology: The iris recognition technology process is generally divided into four steps: iris image acquisition, image preprocessing, feature extraction and feature matching. Iris image acquisition refers to shooting a person's entire eye using a specific digital camera device, and transferring the captured image to a computer via an image capture card. Image preprocessing refers to the fact that the captured eye image includes a lot of redundant information and cannot satisfy the requirements in terms of definition and the like, and it needs to perform preprocessing operations such as image smoothing, edge detection, and image separation. Feature extraction refers to extracting unique feature points from the isolated iris image by a certain algorithm and encoding it. Finally, feature matching refers to matching and verification of feature codes and pre-stored iris image feature codes in the database to achieve the purpose of identification. The iris image capture device, as shown, uses a SOVIC SP-313 camera. The camera adopts the latest CCD effect CMOS sensor, image resolution of 350,000 pixels (640 × 480 without software interpolation), built-in low light auxiliary light source, can minimize the stimulus to the human eye, when used with The artificial darkroom makes the human eye image clearer and brighter. It is the human eye image acquired by the camera used in this design. After the picture data is acquired, it is only necessary to write the picture data in a certain picture format to complete the storage of the desired eye image in the computer. This article uses the BMP format image file because the image data stored in the BMP image file is not compressed, which facilitates the subsequent image preprocessing. Eye Image Preprocessing The BMP image file format mainly includes 1, 4, 8, 16, 24, and 32-bit image formats. The 32-bit BMP image file format indicates that the image has 232 colors. Each pixel in the image is represented by 32 bits. Under normal circumstances, the file format is not a color version, and the highest 8 bits in 32 bits are reserved, and the remaining 8 bits indicate red. , 8 bits are green and 8 bits are blue. An 8-bit BMP image file indicates that the image has 256 colors. Each pixel in the image is represented by 8 bits, and the 8 bits are used as an index to find the color of the pixel in the color table. The 8-bit BMP image is also commonly called a grayscale image. The image captured in this article is a 32-bit color BMP image. 32-bit color images store more image color data, and the size of the image file is also larger. However, from the requirements of image recognition in this paper, these are unnecessary, so it is necessary to convert it to an 8-bit grayscale image. The conversion formula is as follows: Among them, Gray (i, j) is the gray value of the converted black and white image at the (i, j) point. Since the proportion of green in the formula is the largest, the value of G can be used as the gray value after the conversion. degree. The converted grayscale image is as shown. From the image point of view, there is no big difference from the 32-bit RGB image, but the image file size is reduced from 1.17Mb to 301Kb. After the acquired eye image is converted to a grayscale image, the grayscale image needs to be denoised. This paper uses the weighted mean filter in the spatial domain method. It uses a sliding window with odd points to slide over the image. The gray value of the image pixel corresponding to the center point of the window is used as the gray value of each point in the window. Instead of the mean value, if the sliding window specifies the weight of each pixel of the window during the averaging process, that is, the coefficient of each pixel. Extracting iris images This process requires reading the data of the eye image, detecting the inner and outer edges of the iris image, extracting the coordinates and short radius of the inner circle, finding the long radius of the iris, establishing the polar coordinate system, separating the iris image, and finally extracting the features. Compared with other parts of the eye, the gray value of the pupil is much smaller, that is, the color is much darker, and there is a significant mutation in the gray level, which means that the gray level in the pupil is better than other Part of the gray level is "much darker". Therefore, we can make full use of this feature and perform histogram analysis on Figure 2. The results are shown in the figure: From the calculation results, it can be concluded that the gray value of the image starts from 62, and there are several peak points in the graph. We know that the color of the pupil is the darkest, so we can determine that the first peak is the gray distribution of the pupil. Specifically observe the first peak, which is basically a sinusoidal function with 72 as the peak (value: 884), the left 62 (value: 0) as the trough, and the 1/4 cycle as 10. Based on this, we determined that the valley on the right side is 82. According to the analysis results, the binarization of Fig. 4 is performed. The threshold is 82, and the long radius of the iris can be calculated as shown in the following figure. If you can achieve 100% identification, from the biological characteristics of the iris to unlock, it is more secure than fingerprint scanning, Touch ID, in theory, only DNA can exceed it. In the field of human biometrics, the false positive rate is a very important indicator. The lower its numerical value, the more accurate it is to identify and the less likely it is to make mistakes. The false recognition rate of iris recognition is 1/1500000, and that of TouchID is 1/50000. And in terms of uniqueness, when people reach the age of two, the iris of the human eye will hardly change again, so the iris has better “long-term security†as a “passwordâ€. Since the iris unlocking fraud rate is so high and it seems to be so safe, there may be many friends who ask questions. Can the iris unlocking replace fingerprint recognition? In fact, this is an open issue. After all, in the field of mobile phones, fingerprint identification has not yet been truly popularized. For the iris whose identification rate has not yet reached the standard, we do not want to determine the future. Iris lock defect. The safety of iris locks is relative. If a person has the right to unlock the iris at different times, its biological information is left in different places and the risk of being copied is increased. The method of resolving risks should adopt the method of iris plus password card. In this way, if different locks use iris and different password cards, the risk will be greatly reduced. In this way, the convenience of iris recognition has been reduced, but the security has been effectively improved. The above is the smart lock locks the working principle of intelligent iris lock for everyone. Please pay more attention to this site!
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