IrisCode decompression based on the dependence between its bit pairs

IrisCode is an iris recognition algorithm developed in 1993 and continuously improved by Daugman. Understanding IrisCode's properties is extremely important because over 60 million people have been mathematically enrolled by the algorithm. In this paper, IrisCode is proved to be a compression algorithm, which is to say its templates are compressed iris images. In our experiments, the compression ratio of these images is 1:655. An algorithm is designed to perform this decompression by exploiting a graph composed of the bit pairs in IrisCode, prior knowledge from iris image databases, and the theoretical results. To remove artifacts, two postprocessing techniques that carry out optimization in the Fourier domain are developed. Decompressed iris images obtained from two public iris image databases are evaluated by visual comparison, two objective image quality assessment metrics, and eight iris recognition methods. The experimental results show that the decompressed iris images retain iris texture that their quality is roughly equivalent to a JPEG quality factor of 10 and that the iris recognition methods can match the original images with the decompressed images. This paper also discusses the impacts of these theoretical and experimental findings on privacy and security.     

Recompression effects in iris recognition

Rating a compression algorithms' performance is usually done in experimental studies, where researchers have frequently used JPEG pre-compressed data. It is not clear yet, if results of such compression experiments are reliable when conducted on pre-compressed data. To investigate this issue, we first study the impact of using pre-compressed data in iris segmentation and evaluate the relation between iris segmentation performance and general image quality metrics. In this context we propose a method to overcome potential problems in case using pre-compressed data sets cannot be avoided. As the second step, we conduct experimentation on the entire iris recognition pipeline. We find that overall, recognition accuracy results might not be entirely reliable in case of applying JPEG XR or JPEG2000 to JPEG pre-compressed data.     

High-dynamic-range still-image encoding in JPEG 2000

The raw size of a high-dynamic-range (HDR) image brings about problems in storage and transmission. Many bytes are wasted in data redundancy and perceptually unimportant information. To address this problem, researchers have proposed some preliminary algorithms to compress the data, like RGBE/XYZE, OpenEXR, LogLuv, and so on. HDR images can have a dynamic range of more than four orders of magnitude while conventional 8-bit images retain only two orders of magnitude of the dynamic range. This distinction between an HDR image and a conventional image leads to difficulties in using most existing image compressors. JPEG 2000 supports up to 16-bit integer data, so it can already provide image compression for most HDR images. In this article, we propose a JPEG 2000-based lossy image compression scheme for HDR images of all dynamic ranges. We show how to fit HDR encoding into a JPEG 2000 encoder to meet the HDR encoding requirement. To achieve the goal of minimum error in the logarithm domain, we map the logarithm of each pixel value into integer values and then send the results to a JPEG 2000 encoder. Our approach is basically a wavelet-based HDR still-image encoding method.     

Robust memory-efficient data level information fusion of multi-modal biometric images

This paper presents a novel multi-level wavelet based fusion algorithm that combines information from fingerprint, face, iris, and signature images of an individual into a single composite image. The proposed approach reduces the memory size, increases the recognition accuracy using multi-modal biometric features, and withstands common attacks such as smoothing, cropping, JPEG 2000, and filtering due to tampering. The fusion algorithm is validated using the verification algorithms we developed, existing algorithms, and commercial algorithm. In addition to our multi-modal database, experiments are also performed on other well known databases such as FERET face database and CASIA iris database. The effectiveness of the fusion algorithm is experimentally validated by computing the matching scores and the equal error rates before fusion, after reconstruction of biometric images, and when the composite fused image is subjected to both frequency and geometric attacks. The results show that the fusion process reduced the memory required for storing the multi-modal images by 75%. The integrity of biometric features and the recognition performance of the resulting composite fused image is not affected significantly. The complexity of the fusion and the reconstruction algorithms is O(n log n) and is suitable for many real-time applications. We also propose a multi-modal biometric algorithm that further reduces the equal error rate compared to individual biometric images.     

基于自适应稀疏变换的指纹图像压缩

随着指纹识别技术的广泛应用,大量指纹图像需要被收集和存储.在指纹识别系统中,对于大容量的指纹数据库,指纹图像必须经过压缩后存储以减少存储空间,本文提出了基于自适应稀疏变换的指纹图像压缩算法.该算法在离线状态下提取指纹图像特征训练超完备字典;在编码过程中,首先利用差分预测编码和稀疏变换将待压缩指纹图像转换到稀疏域,然后对直流系数和稀疏表达系数进行量化和熵编码,从而实现图像信息的压缩.实验表明,在中低码率段,本文算法相比于JPEG、JPEG2000和WSQ等主流压缩算法表现出更优越的率失真性能;在相同码率时,本文算法生成的压缩图像的主观视觉效果更好,指纹识别率更高.     

Using standard image compression algorithms to store data from computational fluid dynamics

Three-dimensional numerical modeling of fluid flows is an important research tool to understand many fluid dynamical effects observed in nature. With the strong growth of available computational resources the use of such models has greatly increased over the last years. Because the available mass storage has not increased in the same order as the CPU speed many researchers nowadays face the problem of how to store and transfer the large data sets produced by the model calculations for post-processing. The use of lossy wavelet-based compression techniques on this data has been investigated in several publications. These techniques are often specialized to one problem and are not easy to implement. In the area of digital media, however, advances have been made for still image (JPEG, JPEG-2000) and motion image (MPEG) compression. In this paper we investigate the usefulness of these image compression algorithms for the storage of data from computational fluid dynamics on regular cartesian grids. We analyze both the compression ratios achieved and the error introduced by these lossy compression schemes. We found that, for our purposes, the JPEG compression scheme allows an easy-to-use, portable, robust, and computationally efficient lossy compression. For the easy use of these compression algorithms we present a simple wrapper library.     

Fusing the information in visible light and near-infrared images for iris recognition

Automated human identification is a significant issue in real and virtual societies. Iris is a suitable choice for meeting this goal. In this paper, we present an iris recognition system that uses images acquired in both near-infrared and visible lights. These two types of images reveal different textural information of the iris tissue. We demonstrated the necessity to process both VL and NIR images to recognize irides. The proposed system exploits two feature extraction algorithms: one is based on 1D log-Gabor wavelet which gives a detailed representation of the iris region and the other is based on 1D Haar wavelet which represents a coarse model of iris. The Haar wavelet algorithm is proposed in this paper. It makes smaller iris templates than the 1D log-Gabor approach and yet achieves an appropriate recognition rate. We performed the fusion at the match score level and examined the performance of the system in both verification and identification modes. UTIRIS database was used to evaluate the method. The results were compared with other approaches and proved to have better recognition accuracy, while no image enhancement technique is utilized prior to the feature extraction stage. Furthermore, we demonstrated that fusion can compensate the lack of input image information, which can be beneficial in reducing the computation complexity and handling non-cooperative iris images.     

JPEG2000中不同小波基的图像压缩性能分析

主要讨论了用JPEG2000标准进行静态图像压缩时，小波基的选择对图像压缩性能的影响．我们根据JPEG2000标准，比较了各类图像在不同小波基下的压缩结果，发现在JPEG2000中，小波基的选择在一定程度上影响了图像的压缩性能．对于某些图像，JPEG2000标准的缺省小波基并不能取得较优的压缩结果．文中分析了影响图像压缩性能的小波基与图像的特性，在此基础上，给出JPEG2000进行图像压缩时小波基的选择方法．     

基于DICOM标准的大型医学影像分布式存储系统研究

提出了一种符合DICOM标准的大型医学影像分布式存储系统的体系结构。采用多级分布式存储体系,引入JPEG2000压缩技术,结合大型数据库,实现智能、稳定和高效的存储管理。切实解决了医院影像中心PACS目前和未来对大容量／超大容量医学影像数据存储的需求。     

JPEG2000图像压缩算法研究

随着网络和多媒体技术的发展,为满足图像压缩的新要求,静态图像编码新标准JPEG2000随之产生了.基于小波变换的嵌入式图像编码方法则是JPEG2000的基础.分析比较了几种典型的编码算法,突出表现了EBCOT(优化截断点的嵌入块编码)算法的良好性能,这也是JPEG2000标准最终采用EBCOT算法的原因;并通过对JPEG2000中核心算法的研究,阐述了JPEG2000较传统JPEG的优越性以及其不足需改进之处.     

A novel cancellable Iris template generation based on salting approach

The iris has been vastly recognized as one of the powerful biometrics in terms of recognition performance, both theoretically and empirically. However, traditional unprotected iris biometric recognition schemes are highly vulnerable to numerous privacy and security attacks. Several methods have been proposed to generate cancellable iris templates that can be used for recognition; however, these templates achieve lower accuracy of recognition in comparison to traditional unprotected iris templates. In this paper, a novel cancellable iris recognition scheme based on the salting approach is introduced. It depends on mixing the original binary iris code with a synthetic pattern using XOR operation. This scheme guarantees a high degree of privacy/security preservation without affecting the performance accuracy compared to the unprotected traditional iris recognition schemes. Comprehensive experiments on various iris image databases demonstrate similar accuracy to those of the original counterparts. Hence, robustness to several major privacy/security attacks is guaranteed.

     

An Investigation of Scalable SIMD I/O Techniques with Application to Parallel JPEG Compression

The problem inherent with any digital image or digital video system is the large amount of bandwidth required for transmission or storage. This has driven the research area of image compression to develop more complex algorithms that compress images to lower data rates with better fidelity. One approach that can be used to increase the execution speed of these complex algorithms is through the use of parallel processing. In this paper, we address the parallel implementation of the JPEG still-image compression standard on the MasPar MP-1, a massively parallel SIMD computer. We develop two novel byte alignment algorithms which are used to efficiently input and output compressed data from the parallel system, and present results which show real-time performance is possible. We also discuss several applications, such as motion JPEG, that can be used in multimedia systems.     

An efficient color and texture based iris image retrieval technique

This paper proposes a hierarchical approach to retrieve an iris image efficiently from for a large iris database. This approach is a combination of both iris color and texture. Iris color is used for indexing and texture is used for retrieval of iris images from the indexed iris database. An index which is determined from the iris color is used to filter out the images that are not similar to the query image in color. Further, iris texture features of those filtered images, are used to determine the images which are similar to the query image. The iris color information helps to design an efficient indexing scheme based on color indices. The color indices are computed by averaging the intensity values of all red and blue color pixels. Kd-tree is used for real-time indexing based on color indices. The iris texture features are obtained through Speeded Up Robust Features (SURF) algorithm. These features are used to get the query’s corresponding image at the top best match. The performance of the proposed indexing scheme is compared with two well known iris indexing schemes ( [Mehrotra et al., 2010] and [Puhan and Sudha, 2008]) on UPOL (Dobeš, Machala, Tichavský, & Posp?´šil, 2004) and UBIRIS (Proencca & Alexandre, 2005) iris databases. It is observed that combination of iris color and texture improves the performance of iris recognition system.     

多算法融合比较的图像文件索引压缩

近年来由于科技的发展和互联网的兴起,图像资料已被广泛地应用在网络上,而图像压缩不但可以节省图像资料占用的内存空间,并且可以加速其传输速度,因此图像压缩技术目前被广泛应用于医学、手机、数据传输、多媒体影音、互联网络等。这里主要是针对无失真的图像压缩技术,先将原始图像转成256色的GIF格式,然后再建立一个索引矩阵,矩阵中元素是由原色RGB信息对应所组成,利用索引矩阵排序法并配合编码簿,再加上配合LZW、CALIC、JPEG2000、JPEG-LS等不同压缩算法,来比较压缩的效果。     

基于JPEG2000的雷达卡技术研究

介绍了一种应用目前最新静态图像压缩算法JPEG2000实现的雷达卡的方法,首先说明了雷达卡的硬件实现平台;接着重点说明JPEG2000压缩算法,在JPEG2000编码器里详细说明了DC层进和分量变换、离散小波变换、内嵌编码与最佳截断这三大要点,并给出了相应的程序流程图;再说明了JPEG2000算法从PC到DSP的移植与优化;最后给出了雷达图像采集压缩系统的性能测试。     

An effective approach for iris recognition using phase-based image matching

This paper presents an efficient algorithm for iris recognition using phase-based image matching --- an image matching technique using phase components in 2D Discrete Fourier Transforms (DFTs) of given images. Experimental evaluation using CASIA iris image databases (ver. 1.0 and ver. 2.0) and Iris Challenge Evaluation (ICE) 2005 database clearly demonstrates that the use of phase components of iris images makes possible to achieve highly accurate iris recognition with a simple matching algorithm. This paper also discusses major implementation issues of our algorithm. In order to reduce the size of iris data and to prevent the visibility of iris images, we introduce the idea of 2D Fourier Phase Code (FPC) for representing iris information. 2D FPC is particularly useful for implementing compact iris recognition devices using state-of-the-art DSP (Digital Signal Processing) technology.     

基于DSP的JPEG标准编解码系统设计

为了将图像数据高质量地传输,便于图像数据的检索、分析、处理和存储,设计了一个以JPEG标准的静态图像压缩编码系统;将原始图像进行图像压缩编码,同时利用DSP芯片处理速度快的特点,进行核心算法的处理满足系统实时性的要求;经过系统的多次验证,图像压缩编码系统成功地将一个31K大小的BMP格式图像转换为3K大小的JPEG格式图像,完成了10∶1的JPEG标准静态图像压缩;因此将原始图像进行图像压缩编码是解决图像数据量大小与有限存储容量和传输带宽矛盾的最合理方法。     

JPEG2000压缩率控制算法的研究

JPEG2000是一种最新的静止图象压缩标准,此标准采用了很先进的压缩技术使其在压缩性能、渐进传输和灵活性等方面都优于JPEG,并且能够提供优良的压缩率控制功能。通过对压缩率的控制,使码流可在任意位置截断,并可利用截断的码流对图像进行解码,能够很方便地实现图像质量、分辨率的渐进传输。压缩率控制算法对图像压缩质量、编码效率、存贮器占用有直接影响。该文详细阐述了两种压缩率控制算法原理,其中一种是JPEG2000建议的压缩后率失真优化PCRD算法,另一种是优先扫描率分配PSRA算法,并对两种算法在算法复杂度、存贮器消耗和压缩质量进行了比较,最后用实验数据评价了两种算法的有效性。     

无损载体和鲁棒代价结合的JPEG图像鲁棒隐写

目的传统隐写技术在实际社交网络信道上难以保护秘密信息的完整性。在社交网络中,图像往往经过有损压缩信道进行传输,从而导致隐蔽通信失效。为了保证经过压缩信道传输的载密图像鲁棒性,设计安全鲁棒的隐蔽通信技术具有实际应用价值。基于最小化图像信息损失,本文提出无损载体和鲁棒代价结合的JPEG图像鲁棒隐写。方法首先,指出构造无损载体能有效维持隐写安全性和鲁棒性的平衡,对经过压缩信道前后的JPEG图像空域像素块进行差分,构造无损载体以确定鲁棒嵌入域;其次,通过对离散余弦变换(discrete cosine transform, DCT)系数进行"±1"操作,并计算空域信息在压缩传输前后的损失,设计衡量DCT系数抗压缩性能的鲁棒代价;同时,验证在低质量因子压缩信道下鲁棒代价更能区分DCT系数的鲁棒能力,最后,利用校验子格编码(syndrome-trellis code, STC),结合无损载体和鲁棒代价对秘密信息进行嵌入。结果实验在BossBase1.01图像库上进行对比实验,相比于传统JPEG隐写技术,构造无损载体作为嵌入域能有效地将信息平均提取错误率降低24.97%,图像的正确提取成功率提高了21...     

基于JPEG2000压缩域的图像信息隐藏方法

为了与图像压缩标准JPEG2000相适应,提出了两种可直接在JPEG2000压缩域内进行信息隐藏的方法,分别采用JPEG2000编码过程和解码过程进行实现。它们既支持无损压缩,也适用于有损压缩,从而具有较广的适用范围。实验结果表明这两种实现方法具有较好的不可见性和压缩性。