The fingerprint verification system is widely used in mobile devices because of fingerprint's distinctive features and ease of capture. Typically, mobile devices utilize small sensors, which have limited area, to capture fingerprint. Meanwhile, conventional fingerprint feature extraction methods need detailed fingerprint information, which is unsuitable for those small sensors. This paper proposes a novel fingerprint verification method for small area sensors based on deep learning. A systematic method combines deep convolutional neural network (DCNN) in a Siamese network for feature extraction and XGBoost for fingerprint similarity training. In addition, a padding technique also introduced to avoid wraparound error problem. Experimental results show that the method achieves an improved accuracy of 66.6% and 22.6% in the FingerPassDB7 and FVC2006DB1B dataset, respectively, compared to the existing methods.

In this paper, we present our work on the design of a new FPGA architecture targeted for high-performance bit-serial pipeline datapath. Bit-parallel systems require large amount of routing resource which is especially critical in using FPGAs. Their device utilization and operation frequency become low because of large routing penalty. Whereas bit-serial circuits are very efficient in routing, therefore are able to achieve a very high logic utilization. Our proposed FPGA architecture is designed taking into account the structure of bit-serial circuits to optimize the logic and routing architecture. Our FPGA guarantees near 100% logic utilization with a straightforward place and route tool due to high routability of bit-serial circuits and simple routing interconnect architecture. The FPGA chip core which we designed consists of around 200k transistors on 3.5 mm square substrate using 0.5 µm 2-metal CMOS process technology.

Narrow swipe sensor has been widely used in embedded systems such as smart-phone. However, the size of captured image is much smaller than that obtained by the traditional area sensor. Therefore, the limited template coverage is the performance bottleneck of such kind of systems. Aiming to increase the geometry coverage of templates, a novel fingerprint template feature synthesis scheme is proposed in the present study. This method could synthesis multiple input fingerprints into a wider template by clustering the minutiae descriptors. The proposed method consists of two modules. Firstly, a user behavior-based Registration Pattern Inspection (RPI) algorithm is proposed to select the qualified candidates. Secondly, an iterative clustering algorithm Modified Fuzzy C-Means (MFCM) is proposed to process the large amount of minutiae descriptors and then generate the final template. Experiments conducted over swipe fingerprint database validate that this innovative method gives rise to significant improvements in reducing FRR (False Reject Rate) and EER (Equal Error Rate).

The Process Virtual Machine (VM) is typical software that runs applications inside operating systems. Its purpose is to provide a platform-independent programming environment that abstracts away details of the underlying hardware, operating system and allows bytecodes (portable code) to be executed in the same way on any other platforms. The Process VMs are implemented using an interpreter to interpret bytecode instead of direct execution of host machine codes. Thus, the bytecode execution is slower than those of the compiled programming language execution. Several techniques including our previous paper, the “Fetch/Decode Hardware Extension”, have been proposed to speed up the interpretation of Process VMs. In this paper, we propose an additional methodology, the “Hardware Extension with Hybrid Execution” to further enhance the performance of Process VMs interpretation and focus on Register-based model. This new technique provides an additional decoder which can classify bytecodes into either simple or complex instructions. With “Hybrid Execution”, the simple instruction will be directly executed on hardware of native processor. The complex instruction will be emulated by the “extra optimized bytecode software handler” of native processor. In order to eliminate the overheads of retrieving and storing operand on memory, we utilize the physical registers instead of (low address) virtual registers. Moreover, the combination of 3 techniques: Delay scheduling, Mode predictor HW and Branch/goto controller can eliminate all of the switching mode overheads between native mode and bytecode mode. The experimental results show the improvements of execution speed on the Arithmetic instructions, loop & conditional instructions and method invocation & return instructions can be achieved up to 16.9x, 16.1x and 3.1x respectively. The approximate size of the proposed hardware extension is 0.04mm2 (or equivalent to 14.81k gates) and consumes an additional power of only 0.24mW. The stated results are obtained from logic synthesis using the TSMC 90nm technology @ 200MHz.

A new and fast fingerprint classification method based on direction patterns is presented in this paper. This method is developed to be applicable to today's embedded fingerprint authentication system, in which small area sensors are widely used. Direction patterns are well treated in the direction map at block level, where each block consists of 88 pixels. It is demonstrated that the search of directions pattern in specific area, generally called as pattern area, is able to classify fingerprints clearly and quickly. With our algorithm, the classification accuracy of 89% is achieved over 4000 images in the NIST-4 database, slightly lower than the conventional approaches. However, the classification speed is improved tremendously up to about 10 times as fast as conventional singular point approaches.

In this paper, a methodology for designing the architecture of the processor array for wide class of image processing algorithms is proposed. A concept of spatially expanding the SFG description which enables us to handle the problem as merely one-dimensional signal processing is used in constructing the methodology. Problem of I/O interface which is critical in real-time processing is also considered.

A novel Template Updating system for fingerprint verification systems used in mobile applications is introduced in the paper. Based on the proposed method, the system performance is improved much more than the original one. Not only the FRR (False Reject Rate) but also the small overlap problem caused by the very narrow sensor on the mobile phone are solved. Based on the template updating system, templates are replaced with matched inputs towards a target structure which can expand the coverage of templates with large displacement and rotation. By using the test database, the system performance shows the FRR can be reduced by 79% in comparison with the one without template updating procedure. This system was adopted in practical mobile phones in the commercial market in 2009.

A simple extension used to assist the decomposition of task-level concurrency within C programs is presented in this paper. The concurrency decomposition is meant to be used as the point of entry for Multiprocessor System-on-Chips (MPSoC) architectures' design-flow. Our methodology allows the (re)use of readily available reference C programs and enables easy and rapid exploration for various alternatives of task partitioning strategies; a crucial task that greatly influences the overall quality of the designed MPSoC. A test case using a JPEG encoder application has been performed and the results are presented in this paper.

Ultra-wideband (UWB) technology has attracted much attention recently due to its high data rate and low emission power. Its media access control (MAC) protocol, WiMedia MAC, promises a lot of facilities for high-speed and high-quality wireless communication. However, these benefits in turn involve a large amount of computational load, which challenges the traditional uniprocessor architecture based implementation method to provide the required performance. However, the constrained cost and power budget, on the other hand, makes using commercial multiprocessor solutions unrealistic. In this paper, a low-cost and energy-efficient multiprocessor system-on-chip (MPSoC), which tackles at once the aspects of system design, software migration and hardware architecture, is presented for the implementation of UWB MAC layer. Experimental results show that the proposed MPSoC, based on four simple RISC processors and shared-memory infrastructure, achieves up to 45% performance improvement and 65% power saving, but takes 15% less area than the uniprocessor implementation.

In recent years, there is an increasing trend of using biometric identifiers for personal authentication. Encouraged by advances in smart card technologies, the fingerprint matching gets increasingly embedded into smart cards for an effective personal authentication method. However, current generation of low cost smart cards are usually equipped with limited hardware resources such as an 8-bit or 16-bit microcontroller. The fingerprint matching typically is a time consuming, computationally intensive and costly process. Therefore, it is still a challenge to integrate the fingerprint matching into a smart card. In this paper, we present a fast memory-efficient fingerprint matching using minutia ridge shape feature. This feature offers advantages of smaller template size, smaller memory requirement, faster matching time and robust matching against image distortion over conventional minutiae-based feature. The implementation result shows that the proposed method can be embedded in smart cards for a real-time Match-on-Card system.

A new H.263+ rate control method that has very low encoder-decoder delay, small buffer and low computational complexity for hardware realization is proposed in this paper. This method focuses on producing low encoder-decoder delay in order to solve the lip synchronization problem. Low encoder-decoder delay is achieved by improving target bit rate achievement and reducing processing delay. The target bit rate achievement is improved by allocating an optimum frame encoding bits, and employing a new adaptive threshold of zero vector motion estimation. The processing delay is reduced by simplifying quantization parameter computation, applying a new non-zero coefficient distortion measure and utilizing previous frame information in current frame encoding. The simulation results indicate very large number skipped frames reduction in comparison with the test model TMN8. There were 80 skipped frames less than that of TMN8 within a 380 frame sequence during encoding of a very high movement video sequence. The 27 kbps target bit rate is achieved with insignificant difference for various types of video sequences. The simulation results also show that our method successfully allocates encoding bits, maintains small data at the encoder buffer and avoids buffer from overflow and underflow.

Object detection is an essential and expensive process in many computer vision systems. Standard off-the-shelf embedded processors are hard to achieve performance-power balance for implementation of object detection applications. In this work, we explore an Application Specific Instruction set Processor (ASIP) for object detection using Histogram of Oriented Gradients (HOG) feature. Algorithm simplifications are adopted to reduce memory bandwidth requirements and mathematical complexity without losing reliability. Also, parallel histogram generation and on-the-fly Support Vector Machine (SVM) calculation architecture are employed to reduce the necessary cycle counts. The HOG algorithm on the proposed ASIP was accelerated by a factor of 63x compared to the pure software implementation. The ASIP was synthesized for a standard 90nm CMOS library, with a silicon area of 1.31mm2 and 47.8mW power consumption at a 200MHz frequency. Our object detection processor can achieve 42 frames-per-second (fps) on VGA video. The evaluation and implementation results show that the proposed ASIP is both area-efficient and power-efficient while being competitive with commercial CPUs/DSPs. Furthermore, our ASIP exhibits comparable performance even with hard-wire designs.

Orientation field (OF) estimation is a fundamental process in fingerprint authentication systems. In this paper, a novel binary pattern based low-cost OF estimation algorithm is proposed. The new method consists of two modules. The first is block-level orientation estimation and averaging in vector space by pixel level orientation statistics. The second is orientation quantization and smoothing. In the second module, the continuous orientation is quantized into fixed orientations with sufficient resolution (interval between fixed orientations). An effective smoothing scheme on the quantized orientation space is also proposed. The proposed algorithm is capable of stably processing poor-quality fingerprint images and is validated by tests conducted on an adaptive OF matching scheme. The proposed algorithm is also implemented into a fingerprint System on Chip (SoC) to comfirm that it satisfies the strict requirements of embedded system.

It is theoretically impossible to restore the original fingerprint image from a sequence of line images captured by a line sensor. However, in this paper we propose a unique fingerprint-image-generation algorithm, which derives fingerprint images from sequences of line images captured at different swipe speeds by the line sensor. A continuous image representation, called trajectory, is used in modeling distortion of raw fingerprint images. Sequences of line images captured from the same finger are considered as sequences of points, which are sampled on the same trajectory in N-dimensional vector space. The key point here is not to reconstruct the original image, but to generate identical images from the trajectory, which are independent of the swipe speed of the finger. The method for applying the algorithm in a practical application is also presented. Experimental results on a raw fingerprint image database from a line sensor show that the generated fingerprint images are independent of swipe speed, and can achieve remarkable matching performance with a conventional minutiae matcher.

A novel binary line-pattern algorithm for embedded fingerprint authentication system is introduced in this paper. In this algorithm, each line-pattern is a one-dimension binary matrix that describes the alternation pattern of ridge and valley in fingerprint image. Two parallel lines or two cross lines in a certain scope make up related line-pattern pair. Several such line-pattern pairs at different parts of a fingerprint image can describe another intrinsic feature besides traditional minutiae feature. Experimental results showed this algorithm was not only efficient but also effective. Furthermore, a hybrid fingerprint match scheme is also introduced in this paper. It has the following features: (i) minutiae matching is firstly carried out to calculate the similarity score between the query fingerprint and the template fingerprint, and moreover, the translation and rotation parameters are obtained at the same time; (ii) line-pattern algorithm is immediately performed based on the parameters obtained after minutiae matching to get another similarity score; (iii) the final matching score is the combination of the minutiae matching score and the line-pattern matching score. Experiments were conducted on the FVC2002 database and our private database respectively. Both of the results were inspiring. In detail, at the same FAR value, the FRR of this hybrid match algorithm is to be 2-8% lower than only minutiae-based matching algorithm.

Object detection is at the heart of nearly all the computer vision systems. Standard off-the-shelf embedded processors are hard to meet the trade-offs among performance, power consumption and flexibility required by object detection applications. Therefore, this paper presents an Application Specific Instruction set Processor (ASIP) for object detection using AdaBoost-based learning algorithm with Haar-like features as weak classifiers. Algorithm optimizations are employed to reduce memory bandwidth requirements without losing reliability. In the proposed ASIP, Single Instruction Multiple Data (SIMD) architecture is adopted for fully exploiting data-level parallelism inherent to the target algorithm. With adding pipeline stages, application-specific hardware components and custom instructions, the AdaBoost algorithm is accelerated by a factor of 13.7x compared to the optimized pure software implementation. Compared with ARM946 and TMS320C64+, our ASIP shows 32x and 7x better throughput, 10x and 224x better area efficiency, 6.8x and 18.8x better power efficiency, respectively. Furthermore, compared to hard-wired designs, evaluation results show an advantage of the proposed architecture in terms of chip area efficiency while maintain a reliable performance and achieve real-time object detection at 32fps on VGA video.

In this paper, we will show some significant results of the routability analysis of bit-serial pipeline datapath designs based on Rent's rule and Donath's observation. Our results show that all of the tested bit-serial benchmarks have Rent exponent of below 0.4, indicating that the average wiring length of the circuit is expected to be independent of the circuit size. This study provides some important implications on the silicon utilization and time-area efficiency of bit-serial pipeline circuits on FPGAs and ASICs.

In this paper, a systematic method which generates the micro-architecture of Application Specific Instruction Processor (ASIP) is proposed. Different from previous works, the data path and control path are generated from the instruction sequence which is generated by translating the compiled assembly code. A graphical representation method called Register Transfer Graph (RTG) is introduced to describe the micro-operations of instruction sequence. To achieve high performance, we perform micro-operation level scheduling which dynamically assigns the micro-operations of instruction sequence to the control steps. By transforming the architecture using synthesis parameters, design space is explored more extensively. Connection cost is minimized by removing the inefficient data transfer paths.

In Internet of Things (IoT) applications, system-on-chip (SoCs) with embedded processors are widely used. As an embedded processor, RISC-V, which is license-free and has an extensible instruction set, is receiving attention. However, designing such embedded processors requires an enormous effort to achieve a highly efficient microarchitecture in terms of performance, power consumption, and circuit area, as well as the design verification of running complex software, including modern operating systems such as Linux. In this paper, we propose a method for directly describing the RTL structure of a pipelined RISC-V processor with cache memories, a memory management unit (MMU), and an AXI bus interface using the C++ language. This pipelined processor C++ model serves as a functional simulator of the complete RISC-V core, whereas our C2RTL framework translates the processor C++ model into a cycle-accurate RTL description in the Verilog-HDL and RTL-equivalent C model. Our processor design methodology using the C2RTL framework is unique compared to other existing methodologies because both the simulation and RTL models are derived from the same C++ source, which greatly simplifies the design verification and optimization processes. The effectiveness of our design methodology is demonstrated on a RISC-V processor that runs Linux OS on an FPGA board, achieving a significantly short simulation time of the original C++ processor model and RTL-equivalent C model in comparison to a commercial RTL simulator.

This paper proposes a designing algorithm for quadrilateral recursive filters which consist of four quarter-plane filters in the four quadrants. This can realize a perfect zero-phase filtering which is essential for image processing. Furthermore, several parallel processing algorithms capable of performing under very high parallel efficiency are developed on line-connected and mesh-connected processor arrays. By these proposals, the advantage of two-dimensional non-causal zero-phase recursive digital filters is made clear.