For digital imagery, face detection and identification are functions of great importance in wide-ranging applications, including full facial recognition systems. The development and evaluation of unique and existing face detection and face identification applications require a significant amount of data. Increased availability of such data volumes could benefit the formulation and advancement of many biometric algorithms. Here, the utility of using synthetically generated face data to evaluate facial biometry methodologies to a precision that would be unrealistic for a parametrically uncontrolled dataset, is demonstrated. Particular attention is given to similarity metrics, symmetry within and between recognition algorithms, discriminatory power and optimality of pan and/or tilt in reference images or libraries, susceptibilities to variations, identification confidence, meaningful identification mislabelings, sensitivity, specificity, and threshold values. The face identification results, in particular, could be generalized to address shortcomings in various applications and help to inform the design of future strategies.
KEYWORDS: Convolution, Lab on a chip, Chemical mechanical planarization, Java, Biometrics, Detection and tracking algorithms, Oscilloscopes, Iris recognition, Resistors, Energy efficiency
With improved smartphone and tablet technology, it is becoming increasingly feasible to implement powerful biometric
recognition algorithms on portable devices. Typical iris recognition algorithms, such as Ridge Energy Direction (RED),
utilize two-dimensional convolution in their implementation. This paper explores the energy consumption implications
of 12 different methods of implementing two-dimensional convolution on a portable device. Typically, convolution is
implemented using floating point operations. If a given algorithm implemented integer convolution vice floating point
convolution, it could drastically reduce the energy consumed by the processor. The 12 methods compared include 4
major categories: Integer C, Integer Java, Floating Point C, and Floating Point Java. Each major category is further
divided into 3 implementations: variable size looped convolution, static size looped convolution, and unrolled looped
convolution. All testing was performed using the HTC Thunderbolt with energy measured directly using a Tektronix
TDS5104B Digital Phosphor oscilloscope. Results indicate that energy savings as high as 75% are possible by using
Integer C versus Floating Point C. Considering the relative proportion of processing time that convolution is responsible
for in a typical algorithm, the savings in energy would likely result in significantly greater time between battery charges.
KEYWORDS: Video surveillance, Video, Video processing, Field programmable gate arrays, Image processing, Prototyping, Embedded systems, Parallel processing, Digital signal processing, Standards development
FPGA devices with embedded DSP and memory blocks, and high-speed interfaces are ideal for real-time video
processing applications. In this work, a hardware-software co-design approach is proposed to effectively utilize FPGA
features for a prototype of an automated video surveillance system. Time-critical steps of the video surveillance
algorithm are designed and implemented in the FPGAs logic elements to maximize parallel processing. Other non timecritical
tasks are achieved by executing a high level language program on an embedded Nios-II processor. Pre-tested and
verified video and interface functions from a standard video framework are utilized to significantly reduce development
and verification time. Custom and parallel processing modules are integrated into the video processing chain by Altera's
Avalon Streaming video protocol. Other data control interfaces are achieved by connecting hardware controllers to a
Nios-II processor using Altera's Avalon Memory Mapped protocol.
In the past two years the processing power of video graphics cards has quadrupled and is approaching super computer
levels. State-of-the-art graphical processing units (GPU) boast of theoretical computational performance in the range of
1.5 trillion floating point operations per second (1.5 Teraflops). This processing power is readily accessible to the
scientific community at a relatively small cost. High level programming languages are now available that give access to
the internal architecture of the graphics card allowing greater algorithm optimization. This research takes memory access
expensive portions of an image-based iris identification algorithm and hosts it on a GPU using the C++ compatible
CUDA language. The selected segmentation algorithm uses basic image processing techniques such as image inversion,
value squaring, thresholding, dilation, erosion and memory/computationally intensive calculations such as the circular
Hough transform. Portions of the iris segmentation algorithm were accelerated by a factor of 77 over the 2008 GPU
results. Some parts of the algorithm ran at speeds that were over 1600 times faster than their CPU counterparts. Strengths
and limitations of the GPU Single Instruction Multiple Data architecture are discussed. Memory access times, instruction
execution times, programming details and code samples are presented as part of the research.
KEYWORDS: Field programmable gate arrays, Iris, Iris recognition, Databases, Clocks, Image processing, Signal processing, Personal protective equipment, C++, Computing systems
General purpose computer designers have recently begun adding cores to their processors in order to increase performance. For example, Intel has adopted a homogeneous quad-core processor as a base for general purpose computing. PlayStation3 (PS3) game consoles contain a multicore heterogeneous processor known as the Cell, which is designed to perform complex image processing algorithms at a high level. Can modern image-processing algorithms utilize these additional cores? On the other hand, modern advancements in configurable hardware, most notably field-programmable gate arrays (FPGAs) have created an interesting question for general purpose computer designers. Is there a reason to combine FPGAs with multicore processors to create an FPGA multicore hybrid general purpose computer? Iris matching, a repeatedly executed portion of a modern iris-recognition algorithm, is parallelized on an Intel-based homogeneous multicore Xeon system, a heterogeneous multicore Cell system, and an FPGA multicore hybrid system. Surprisingly, the cheaper PS3 slightly outperforms the Intel-based multicore on a core-for-core basis. However, both multicore systems are beaten by the FPGA multicore hybrid system by >50%.
The iris is currently believed to be one of the most accurate biometrics for human identification. The majority of fielded iris identification systems use fractional Hamming distance to compare a new feature template to a stored database. Fractional Hamming distance is extremely fast, but mathematically weights all regions of the iris equally. Research has shown that different regions of the iris contain varying levels of discriminatory information when using circular boundary assumptions. This research evaluates four statistical metrics for accuracy improvements on low resolution and poor quality images. Each metric statistically weights iris regions in an attempt to use the iris information in a more intelligent manner. A similarity metric extracted from the output stage of an artificial neural network demonstrated the most promise. Experiments were performed using occluded, subsampled, and motion blurred images from the CASIA, University of Bath, and ICE 2005 databases. The neural network-based metric improved accuracy at nearly every operating point.
One of the basic challenges to robust iris recognition is iris segmentation. This paper proposes the
use of an artificial neural network and a feature saliency algorithm to better localize boundary pixels of the iris.
No circular boundary assumption is made. A neural network is used to near-optimally combine current iris
segmentation methods to more accurate localize the iris boundary. A feature saliency technique is performed to
determine which features contain the greatest discriminatory information. Both visual inspection and
automated testing showed greater than 98 percent accuracy in determining which pixels in an image of the eye
were iris pixels when compared to human determined boundaries.
KEYWORDS: Facial recognition systems, Field programmable gate arrays, Video, Sensors, Detection and tracking algorithms, Statistical analysis, Image processing, Video surveillance, Digital signal processing, Computer simulations
The first step in a facial recognition system is to find and extract human faces in a static image or video frame. Most face
detection methods are based on statistical models that can be trained and then used to classify faces. These methods are
effective but the main drawback is speed because a massive number of sub-windows at different image scales are
considered in the detection procedure. A robust face detection technique based on an encoded image known as an
"integral image" has been proposed by Viola and Jones. The use of an integral image helps to reduce the number of
operations to access a sub-image to a relatively small and fixed number. Additional speedup is achieved by incorporating
a cascade of simple classifiers to quickly eliminate non-face sub-windows. Even with the reduced number of accesses to
image data to extract features in Viola-Jones algorithm, the number of memory accesses is still too high to support realtime
operations for high resolution images or video frames. The proposed hardware design in this research work
employs a modular approach to represent the "integral image" for this memory-intensive application. An efficient
memory manage strategy is also proposed to aggressively utilize embedded memory modules to reduce interaction with
external memory chips. The proposed design is targeted for a low-cost FPGA prototype board for a cost-effective face
detection/recognition system.
Iris recognition algorithms depend on image processing techniques for proper segmentation of the iris. In the Ridge
Energy Direction (RED) iris recognition algorithm, the initial step in the segmentation process searches for the pupil by
thresholding and using binary morphology functions to rectify artifacts obfuscating the pupil. These functions take
substantial processing time in software on the order of a few hundred million operations. Alternatively, a hardware
version of the binary morphology functions is implemented to assist in the segmentation process. The hardware binary
morphology functions have negligible hardware footprint and power consumption while achieving speed up of 200 times
compared to the original software functions.
Iris recognition is an increasingly popular biometric due to its relative ease of use and high reliability. However, commercially available systems typically require on-axis images for recognition, meaning the subject is looking in the direction of the camera. The feasibility of using off-axis images is an important area of investigation for iris systems with more flexible user interfaces. The authors present an analysis of two image transform processes for off-axis images and an analysis of the utility of correcting for cornea refraction effects. The performance is assessed on the U.S. Naval Academy iris image database using the Ridge Energy Direction recognition algorithm developed by the authors, as well as with a commercial implementation of the Daugman algorithm.
The iris contains fibrous structures of various sizes and orientations which can be used for human identification.
Drawing from a directional energy iris identification technique, this paper investigates the size, orientation, and location
of the iris structures that hold stable discriminatory information. Template height, template width, filter size, and the
number of filter orientations were investigated for their individual and combined impact on identification accuracy.
Further, the iris was segmented into annuli and radial sectors to determine in which portions of the iris the best
discriminatory information is found. Over 2 billion template comparisons were performed to produce this analysis.
The iris is currently believed to be the most accurate biometric for human identification. The majority of fielded iris
identification systems are based on the highly accurate wavelet-based Daugman algorithm. Another promising
recognition algorithm by Ives et al uses Directional Energy features to create the iris template. Both algorithms use
Hamming distance to compare a new template to a stored database. Hamming distance is an extremely fast computation,
but weights all regions of the iris equally. Work from multiple authors has shown that different regions of the iris contain
varying levels of discriminatory information. This research evaluates four post-processing similarity metrics for
accuracy impacts on the Directional Energy and wavelets based algorithms. Each metric builds on the Hamming distance
method in an attempt to use the template information in a more salient manner. A similarity metric extracted from the
output stage of a feed-forward multi-layer perceptron artificial neural network demonstrated the most promise. Accuracy
tables and ROC curves of tests performed on the publicly available Chinese Academy of Sciences Institute of
Automation database show that the neural network based distance achieves greater accuracy than Hamming distance at
every operating point, while adding less than one percent computational overhead.
The human iris is perhaps the most accurate biometric
for use in identification. Commercial iris recognition systems currently
can be found in several types of settings where a person’s
true identity is required: to allow passengers in some airports to be
rapidly processed through security; for access to secure areas; and
for secure access to computer networks. The growing employment
of iris recognition systems and the associated research to develop
new algorithms will require large databases of iris images. If the
required storage space is not adequate for these databases, image
compression is an alternative. Compression allows a reduction in
the storage space needed to store these iris images. This may, however,
come at a cost: some amount of information may be lost in the
process. We investigate the effects of image compression on the
performance of an iris recognition system. Compression is performed
using JPEG-2000 and JPEG, and the iris recognition algorithm
used is an implementation of the Daugman algorithm. The
imagery used includes both the CASIA iris database as well as the
iris database collected by the University of Bath. Results demonstrate
that compression up to 50:1 can be used with minimal effects
on recognition.
Video surveillance is ubiquitous in modern society, but surveillance cameras are severely limited in utility by their low
resolution. With this in mind, we have developed a system that can autonomously take high resolution still frame
images of moving objects. In order to do this, we combine a low resolution video camera and a high resolution still
frame camera mounted on a pan/tilt mount. In order to determine what should be photographed (objects of interest), we
employ a hierarchical method which first separates foreground from background using a temporal-based median
filtering technique. We then use a feed-forward neural network classifier on the foreground regions to determine
whether the regions contain the objects of interest. This is done over several frames, and a motion vector is deduced for
the object. The pan/tilt mount then focuses the high resolution camera on the next predicted location of the object, and
an image is acquired. All components are controlled through a single MATLAB graphical user interface (GUI). The
final system we present will be able to detect multiple moving objects simultaneously, track them, and acquire high
resolution images of them. Results will demonstrate performance tracking and imaging varying numbers of objects
moving at different speeds.
A one-dimensional approach to iris recognition is presented. It is translation-, rotation-, illumination-, and scale-invariant. Traditional iris recognition systems typically use a two-dimensional iris signature that requires circular rotation for pattern matching. The new approach uses the Du measure as a matching mechanism, and generates a set of the most probable matches (ranks) instead of only the best match. Since the method generates one-dimensional signatures that are rotation-invariant, the system could work with eyes that are tilted. Moreover, the system will work with less of the iris than commercial systems, and thus could enable partial-iris recognition. In addition, this system is more tolerant of noise. Finally, this method is simple to implement, and its computational complexity is relatively low.
In this paper, we investigate the accuracy of using a partial iris image for identification and determine which portion of the iris has the most distinguishable patterns. Moreover, we compare these results with the results of Du et. al. using the CASIA database. The experimental results show that it is challenging but feasible to use only a partial iris image for human identification.
An iris identification algorithm is proposed based on adaptive thresholding. The iris images are processed fully in the spatial domain using the distinct features (patterns) of the iris. A simple adaptive thresholding method is used to segment these patterns from the rest of an iris image. This method could possibly be utilized for partial iris recognition since it relaxes the requirement of using a majority of the iris to produce an iris template to compare with the database. In addition, the simple thresholding scheme can improve the computational efficiency of the algorithm. Preliminary results have shown that the method is very effective. However, further testing and improvements are envisioned.
A novel approach to iris recognition is proposed in this paper. It differs from traditional iris recognition systems in that it generates a one-dimensional iris signature that is translation, rotation, illumination and scale invariant. The Du Measurement was used as a matching mechanism, and this approach generates the most probable matches instead of only the best match. The merit of this method is that it allows users to enroll with or to identify poor quality iris images that would be rejected by other methods. In this way, the users could potentially identify an iris image by another level of analysis. Another merit of this approach is that this method could potentially improve iris identification efficiency. In our approach, the system only needs to store a one-dimensional signal, and in the matching process, no circular rotation is needed. This means that the matching speed could be much faster.
SAR produce coherent, and speckled, high resolution images of the ground. Because modern systems can generate large amounts of imagery, there is substantial interest in applying image compression techniques to these products. In this paper, we examine the properties of speckled imagery relevant to the task of data compression. In particular, we demonstrate the advisability of compressing the speckle mean function rather than the literal image. The theory, methodology, and an example are presented.
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