Figure 1: Schematic diagram of the building management system based on biometric authentication” title=”bioauthentication” > biometric authentication technology.
Converged processors used in biometric authentication
In practical applications, almost all biometric authentication technologies are implemented through the following steps: sensors collect raw biometric data; process the collected data, complete feature extraction, and form a feature set representing the target object; pattern matching, compare the extracted feature set with the database Compare the templates saved in the user interface; the judgment program judges whether the identity declared by the user can pass the verification according to the comparison result. For a portable biometric authentication terminal, all these steps must be completed by the processor in a very short time, thus placing high demands on the processor performance.
In recent years, ADI’s Blackfin series of converged processors have been used in many leading biometric authentication systems around the world. Blackfin Processors” title=”Blackfin Processors”>Blackfin Processors are a new class of 16/32-bit embedded processors designed to meet the computing requirements and power consumption constraints of today’s embedded audio, video and communications applications The Blackfin processor combines a 32-bit RISC-type instruction set and dual 16-bit multiply-accumulate (MAC) signal processing with the ease of use found in general-purpose microcontrollers. This combination of processing features enables Blackfin to process The device has outstanding advantages in both signal processing and control processing applications. In many applications, the addition of a separate heterogeneous processor can be avoided, and complex digital signal processing operations involved in various biometric authentication methods can be easily implemented.
Blackfin processors have been widely used in video and image processing applications, and image processing is the basic technology of almost all biometric authentication systems. Taking fingerprint recognition as an example, its preprocessing mainly includes fingerprint image enhancement, fingerprint image binarization, fingerprint image refinement and post-processing of fingerprint image refinement, all of which depend on the image processing capability of the processor. In particular, the following unique features of the Blackfin processor provide important support for the implementation of biometric authentication technology: The Blackfin processor supports 8-bit data and word lengths commonly used by many pixel processing algorithms, greatly improving iris recognition, face recognition Common dynamic image processing and pixel value processing in other applications; Blackfin has L1 and L2 two-level cache structure; due to its fast reading speed, this cache structure can effectively improve the processing speed of biometric parameters; biometric authentication requires image processing For processing, a large amount of memory data access is involved, and the DMA controller of the Blackfin processor can automatically complete the data transfer, requiring very little processor core overhead, saving the precious computing power of the processor.
Figure 2: Blackfin processor core architecture.
Currently, Blackfin processors offer up to 600MHz of performance in single-core products. The Blackfin processor family also offers industry-leading power consumption performance down to 0.8V. Unlike other processors that only allow adjustment of the operating frequency, the Blackfin processor allows designers to adjust both voltage and frequency to minimize power consumption. Biometric authentication is widely used in battery-operated portable devices, so this combination of high performance and low power consumption is essential for biometric authentication applications today and in the future.
Biometric authentication involves a wide range of technologies and products, and there are many relevant convergent processor application cases, including the world’s first innovative iris recognition mouse launched by Qritek; FingerLoc AFS 8600 embedded fingerprint recognition device from AuthenTec; Unifinger from Suprema SFM3000 and SFM3500 fingerprint identification modules; SecureTouch Advanced System (STAm) of Biometric Access Company, etc. This article takes the world’s first iris recognition mouse product as an example to introduce the reference ideas for product design and solution selection based on the Blackfin processor.
Design of Mouse Iris Recognition System
Sensitive data is stored in the computer, how to effectively prevent unauthorized users from using the computer? Qritek, based in Seoul, South Korea, uses its innovative IRIBIO mouse iris recognition system to help you “keep your eyes on” the computer you’re using. Qritek’s IRIBIO mouse system works as follows: the user picks up the mouse, looks into the concave mirror to focus the eye, and the iris camera on the mouse begins to calculate the position of the eye, based on the behavior of the eye, light sensitivity, size, and eye shape in Eastern and Western countries. Then, the camera performs black and white imaging and performs a lot of related processing; finally, the obtained iris information is checked with the template stored in the database.
The system uses a tiny camera and an embedded iris authentication engine circuit board, all housed in a compact computer mouse, physically and logically separated from the host computer. Therefore, it is significantly different from other biometric authentication systems. The IRIBIO mouse does not register or store your important personal biometric data on the computer hard drive. At the same time, the built-in iris recognition operation is implemented on the microprocessor in the mouse. The identification process is carried out inside the mouse, which is separated from the PC, which can avoid being infected by viruses or stolen by network hackers. The software embedded in the mouse uses the user’s unique iris pattern as a password to process authentication.
Figure 3: Schematic diagram of the IRIBIO mouse application based on the Blackfin processor.
The ultimate goal of Qritek’s selection of processors for this product is to provide users with a fast but cost-effective authentication solution. The processor embedded in the IRIBIO mouse not only needs to undertake many functions in a very small footprint, but also needs to be reasonably priced and low power consumption. For Qritek, fast video processing is very important, and its BF533 processor running at 500 MHz not only provides the required performance, but also at an affordable price. The processor has 148KB of on-chip memory, another attractive feature for Qritek’s IRIBIO mouse products, which can easily meet the storage space requirements of Qritek’s iris recognition algorithm.
The BF533 combines a high-performance core with industry-standard interfaces, thereby eliminating the need for high-cost external components for Qritek. System peripherals built into Blackfin processors include UART ports, SPI ports, two serial ports (SPORTs) and four general-purpose timers, a real-time clock, a watchdog timer, and a parallel peripheral interface (PPI), Thereby, the system expansion characteristics of the processor are enhanced. Qritek uses the I/O capabilities of the Blackfin processor to control white LEDs and infrared LEDs and connect CMOS sensors.
The combination of the high-speed performance of the Blackfin processor and the fast algorithm of Qritek makes the registration of the iris information of this product only take 7-10 seconds, while the iris recognition takes only 1-2 seconds, which has a very obvious performance advantage among similar products.
The choice of any product solution cannot be limited to performance without other factors, and the implementation cost of the solution and supporting technical support are also very important.
In addition to using ADI’s Blackfin processor, Suprema takes full advantage of its simulation tools and uses VisualDSP++ software, ADI’s integrated software development and debugging environment, which enables Suprema to manage its Design. In addition, Suprema has adopted the VisualDSP++ Kernel (VDK) library for task scheduling and communication between processors, enabling Suprema to take advantage of advanced scheduling and resource allocation methods to specifically address memory allocation and timing constraints. These tools help Suprema use routine code more efficiently, eliminating the need to design from scratch, thus saving development and debugging time.
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