Secure multimodal biometric authentication with wavelet quantization based fingerprint watermarking

As malicious attacks greatly threaten the security and reliability of biometric systems, ensuring the authenticity of biometric data is becoming increasingly important. In this paper we propose a watermarking-based two-stage authentication framework to address this problem. During data collection, face features are embedded into a fingerprint image of the same individual as data credibility token and secondary authentication source. At the first stage of authentication, the credibility of input data is established by checking the validness of extracted patterns. Due to the specific characteristics of face watermarks, the face detection based classification strategies are introduced for reliable watermark verification instead of conventional correlation based watermark detection. If authentic, the face patterns can further serve as supplemental identity information to facilitate subsequential biometric authentication. In this framework, one critical issue is to guarantee the robustness and capacity of watermark while preserving the discriminating features of host fingerprints. Hence a wavelet quantization based watermarking approach is proposed to adaptively distribute watermark energy on significant DWT coefficients of fingerprint images. Experimental results which evaluate both watermarking and biometric authentication performance demonstrate the effectiveness of this work.     

Double watermarking‐based biometric access control for radio frequency identification card

The integration of data privacy and security into radio frequency identification (RFID) technology, particularly into RFID tags, has become one of the most attractive research areas. A crucial challenge in RFID technology research lies in providing an efficient protection for systems against information theft and illegitimate access. This article proposes a secure solution based on an RFID card for physical biometric access‐control applications. This is done by integrating two biometric modalities, namely face and fingerprint which are secured via a double watermarking technique. The suggested approach is ensured by two levels of watermarking. At the first level, the wavelet packet decomposition watermarking algorithm is used to insert features from the fingerprint (minutiae) in the face image of an authorized person. At the second level, the same watermarking algorithm is employed to insert the fingerprint watermark in the face features extracted by Gabor filters from the previously watermarked face image (at the first level). The obtained secured watermarked biometric data are then integrated in a 1‐kB high frequency proximity RFID card. This combination of both RFID technology and the double watermarking technique provides a biometric control access framework. Compared with the state‐of‐the‐art frameworks, the proposed one ensures a good compromise between a reduced computational complexity and a high level of data security while maintaining a small space of storage and a low cost compared to those of the marketed products.     

A multimodal biometric watermarking system for digital images in redundant discrete wavelet transform

The traditional watermarking algorithms prove the rightful ownership via embedding of independent watermarks like copyright logos, random noise sequences, text etc into the cover images. Coupling biometrics with watermarking evolved as new and secure approach as it embeds user specific biometric traits and thus, narrows down the vulnerability to impostor attacks. A multimodal biometric watermarking system has been proposed in this paper in the redundant discrete wavelet transform(RDWT). Two biometric traits of the user i.e. the iris and facial features are embedded independently into the sub-bands of the RDWT of cover image taking advantage of its translation invariant property and sufficient embedding capacity. The ownership verification accuracy of the proposed system is tested based on the individual biometric traits as well as the fused trait. The accuracy was enhanced while using the fused score for evaluation. The security of the scheme is strengthened with usage of non-linear chaotic maps, randomization via Hessenberg decomposition, Arnold scrambling and multiple secret keys. The robustness of the scheme has been tested against various attacks and the verification accuracy evaluated based on false acceptance rate, false rejection rate, area under curve and equal error rate to validate the efficacy of the proposed scheme.

     

Tamper detection and self-recovery of biometric images using salient region-based authentication watermarking scheme

Biometric images can be split into regions of salient (ROI) and regions of background (ROB) based on salient region detection. During the process of watermark embedding, salient regions which contain rich-information are less affected by the watermark, therefore can be embedded into more watermarks, and regions of background (ROB) are susceptible to the effect of watermarks, so that they should be embedded lighter or even none in order to preserve the recognition quality of biometric images. In this paper, a novel scheme for tamper detection and self-recovery of biometric images using salient region-based authentication watermarking is proposed. Firstly, we propose a novel multi-level authentication watermarking scheme, which is used to verify the integrity of biometric images. Secondly, biometric data of these biometric images which is used as information watermarks is embedded into themselves. As a result, verification systems can recover the damaged data of original biometric images with hidden information based on tampering detection result. Experimental results and theoretic analysis show that our proposed scheme can detect tampered regions, and recover biometric data while keeping the recognition quality.     

Fusion-based encryption scheme for cancelable fingerprint recognition

This paper presents a fingerprint image encryption scheme based on fingerprint image fusion with another visible image that is rich in details. The encryption process is performed with chaotic Baker map, which has large immunity to noise. The image fusion process is performed with the Haar wavelet transform, and it can be implemented with the average or maximum fusion rule. The fusion process is performed, because fingerprint images are not rich in details, and hence the direct application of chaotic Baker map encryption will not be efficient for encrypting this type of images. To obtain an image that is rich in details, it is possible to use another encrypted image with a strong ciphering algorithm such as the RC6. Several perspectives are considered for performance evaluation of the proposed encryption scheme including visual inspection, histogram analysis, correlation coefficient, entropy analysis, processing time, and the effect of noise after decryption. The proposed fingerprint encryption scheme is appropriate for cancelable biometric applications to preserve the privacy of users by keeping their original fingerprints away from usage in the recognition system. The simulation results demonstrate that the proposed image encryption scheme gives a proficient and secure path for unique encrypted fingerprints. Both Equal Error Rate (EER) and Area under Receiver Operating Characteristic (AROC) curve are used for performance evaluation of the proposed cancelable fingerprint recognition scheme revealing high performance.

     

Perceptual Digital Watermarking for Image Authentication in Electronic Commerce

Digital watermarking has been presented to protect the copyright of electronic multimedia data. The previous proposed watermarking algorithms mostly embed sequences of random numbers or binary images as the watermarks. In this paper, an image accreditation technique by embedding digital gray-level image watermarks in images is proposed. In the proposed method, the host image and the watermark are decomposed into wavelet coefficients. The contextual energies of the host-image wavelet coefficients are computed to select embedding coefficients. The watermark wavelet coefficients are sorted and then embedded in the selected host coefficients. The experiments show that the proposed method provides extra robustness against JPEG-compression, image-processing, and even composite attacks compared to the traditional embedding methods. Moreover, the proposed method has no need of the original image to extract the embedded watermarks. The proposed method facilitates electronic commerce application developers to select adequate digital watermarking techniques for their development systems.     

Hiding biometric data

With the wide spread utilization of biometric identification systems, establishing the authenticity of biometric data itself has emerged as an important research issue. The fact that biometric data is not replaceable and is not secret, combined with the existence of several types of attacks that are possible in a biometric system, make the issue of security/integrity of biometric data extremely critical. We introduce two applications of an amplitude modulation-based watermarking method, in which we hide a user's biometric data in a variety of images. This method has the ability to increase the security of both the hidden biometric data (e.g., eigen-face coefficients) and host images (e.g., fingerprints). Image adaptive data embedding methods used in our scheme lead to low visibility of the embedded signal. Feature analysis of host images guarantees high verification accuracy on watermarked (e.g., fingerprint) images.     

一种基于EMD的人脸图像光照问题处理方法

鉴于经验模态分解（EMD）算法的无参数特性和基于数据驱动的自适应分析,从EMD算法筛选过程中插值点和差值算法选取的角度出发,提出了一种标准EMD的改进算法。用改进的EMD算法分解需要识别的人脸图像,得到从整体上有效消除了光照变化影响的原人脸图像的重构图像,并将重构图像用于识别。通过在Yale B人脸库上的实验结果表明,将提出的光照处理方法应用到人脸识别中可以取得较好的效果。     

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.     

Biometric template protection with DCT-based watermarking

In this paper we have addressed a solution of two big issues in design of multimodal system: template protection and fusion strategy. A robust biometric watermarking algorithm is proposed for biometric template protection. The fingerprint feature vector and iris features are used as watermark. Proposed DCT-based watermarking technique embeds watermark in low-frequency AC coefficients of selected 8 $\times $ 8 DCT smoother blocks. Blocks are classified based on human visual system. The robustness of the proposed algorithm is compared with the few state-of-art literature when watermarked image is subjected to possible channel attacks. Decision level fusion strategy is used to improve the overall performance of multimodal system. That is achieved by conditionally limiting the threshold of the fingerprint system to a maximum value, obtained by projecting 50 % of the cross over error rate on to the FRR curve of the iris system.     

Multiscale Derivative Transform and Its Application to Image Watermarking

A transform that estimates the first and higher-order derivatives of images at multiple scales is proposed. The proposed transform, called Multi-Scale Derivative Transform (MSDT), is specially designed for image watermarking applications. To calculate the first and higher-order image derivatives, MSDT uses the detail wavelet coefficients of the image. Unlike traditional wavelet-based image derivative estimators that use only the horizontal and vertical wavelet coefficients, the proposed transform maps the diagonal as well as the horizontal and vertical wavelet coefficients to the horizontal and vertical derivatives of the image. The inverse transform is designed such that any change in the image derivative domain results in the minimum possible change in the wavelet coefficients. This renders a watermark, that is embedded in the derivative domain, less visible in the image domain. The application of this transform to image watermarking is discussed, and the results are compared with those obtained using traditional wavelet-based image derivative estimators.     

A fast feature selection technique in multi modal biometrics using cloud framework

Biometrics refers to the process that uses biological or physiological traits to identify individuals. The progress seen in technology and security has a vital role to play in Biometric recognition which is a reliable technique to validate individuals and their identity. The biometric identification is generally based on either their physical traits or their behavioural traits. The multimodal biometrics makes use of either two or more of the modalities to improve recognition. There are some popular modalities of biometrics that are palm print, finger vein, iris, face or fingerprint recognition. Another important challenge found with multimodal biometric features is the fusion, which could result in a large set of feature vectors. Most biometric systems currently use a single model for user authentication. In this existing work, a modified method of heuristics that is efficiently used to identify an optimal feature set that is based on a wrapper-based feature selection technique. The proposed method of feature selection uses the Ant Colony Optimization (ACO) and the Particle Swarm Optimization (PSO) are used to feature extraction and classification process utilizes the integration of face, and finger print texture patterns. The set of training images is converted to grayscale. The crossover operator is applied to generate multiple samples for each number of images. The wok proposed here is pre-planned for each weight of each biometric modality, which ensures that even if a biometric modality does not exist at the time of verification, a person can be certified to provide calculated weights the threshold value. The proposed method is demonstrated better result for fast feature selection in bio metric image authentication and also gives high effectiveness security.     

Robust watermarking of fingerprint images

This paper introduces two spatial methods in order to embed watermark data into fingerprint images, without corrupting their features. The first method inserts watermark data after feature extraction, thus preventing watermarking of regions used for fingerprint classification. The method utilizes an image adaptive strength adjustment technique which results in watermarks with low visibility. The second method introduces a feature adaptive watermarking technique for fingerprints, thus applicable before feature extraction. For both of the methods, decoding does not require original fingerprint image. Unlike most of the published spatial watermarking methods, the proposed methods provide high decoding accuracy for fingerprint images. High data hiding and decoding performance for color images is also observed.     

无方向的三角形匹配指纹识别

目的 指纹匹配是自动指纹识别系统研究的核心内容之一,匹配算法的好坏直接影响识别系统的效能。目前,大多数点模式匹配算法都依赖于指纹方向场的求取,由于输入的指纹图像存在平移、旋转和尺度变化,因此同一个手指在不同时间获得的指纹图像的方向场是不同的,这不仅增加了计算量,也影响了指纹识别的精度。针对上述问题,提出了无方向的三角形匹配算法。方法 提出的三角形匹配算法是以平面中任意点与一个确定的三角形之间的位置结构稳定性为理论基础的。首先,分别在待识指纹图像和模板指纹图像中确定基准三角形;其次,将各个特征点与基准三角形三个顶点的距离组成有序三数组;最后,利用数组的相等程度对指纹相似度进行匹配判断。结果 采用国际标准测试库FVC2004进行综合性能比对实验,实验结果表明,与其他几种匹配算法相比,本文方法在识别精度上提高了27.97%~33.81%,在比对时间上降低了3%~5%,在不同旋转角度下误匹配率平均降低了约86.63%,对噪声、平移、旋转和形变有足够的适应能力,具有较高的容错能力和鲁棒性。结论 无方向的三角形匹配算法是一种全局模式的算法,该算法不受指纹图像方向及其位置的影响,实现过程简单,识别精度高,平均比对时间少,适用于处理不同类型的图像数据。     

Image subband coding using fuzzy inference and adaptive quantization

Wavelet image decomposition generates a hierarchical data structure to represent an image. Recently, a new class of image compression algorithms has been developed for exploiting dependencies between the hierarchical wavelet coefficients using zerotrees. This paper deals with a fuzzy inference filter for image entropy coding by choosing significant coefficients and zerotree roots in the higher frequency wavelet subbands. Moreover, an adaptive quantization is proposed to improve the coding performance. Evaluating with the standard images, the proposed approaches are comparable or superior to most state-of-the-art coders. Based on the fuzzy energy judgment, the proposed approaches can achieve an excellent performance on the combination applications of image compression and watermarking.     

Robust and transparent watermarking scheme for colour images

In this study, a robust and transparent watermarking scheme for colour images is proposed. The colour features for the human visual system are utilised to design the colour watermarking scheme. Through the exploitation of the perceptual redundancy of colour images, the proposed watermarking scheme is perceptually tuned to embed and detect the watermark in the perceptually significant sub-bands of luminance and chrominance components of colour images in the wavelet domain. The employment of the uniformity in the uniform colour space and the masking effect mainly due to local variations in luminance magnitude leads to that the perceptual redundancy of colour images can be measured. By using the estimated perceptual redundancy in the form of error visibility thresholds of wavelet coefficients of the colour image, high strength watermarks are invisibly embedded into coefficients of the host colour image for resisting compression and malicious attacks. Simulation results show that the estimation of perceptual redundancy is helpful to the design of the watermarking scheme for colour images. The performance in terms of robustness and transparency of the proposed watermarking scheme is superior to that of the existing scheme.     

一种相移键控数字图像水印算法

根据相移键控原理．提出了一种不可逆DWT域二相相位键控数字图像水印算法．水印信号为二值图像，用不同相位的正弦信号将水印调制后嵌入到数字图像DWT变换系数中；用提取的同频率正弦信号的相位进行水印提取；该方法嵌入的水印具有非常好的安全性，且水印提取不需要原始未加水印的图像．实验结果表明该方法具有很好的不可见性；对JPEG压缩、图像加噪声和图像滤波等常见的图像处理具有极强的鲁棒性．     

基于Bandelet与自适应矩阵范数的全频率无损水印方法

数字水印技术是一种新型的数字图像版权认证和保护技术.传统的数字水印一般通过修改图像数据达到信息隐藏的目的,不适合用于保护不允许修改的图像数据,无损数字水印为这类数据的保护提供了一种有效的方法.提出了一种无需对图像数据做修改的全频率无损数字水印实现方法.首先对图像实施小波变换,变换后的中高频部分采用Bandelet跟踪图像几何流,将纹理和边缘作为图像的特征从而构造出图像高频无损水印参数.对于小波变换的低频部分,通过选取最优的矩阵范数,得到低频系数水印参数生成方案,达到提取图像统计和边缘特征,对图像实现全面保护的目的.实验证明该算法具有良好的抗攻击能力,能够广泛用于对不可修改数据的保护.     

小波-Lagrange方法进行医学图像层间插值

目的 医学图像3维重建通常需要进行层间插值.现有的插值方法虽然种类较多,但在进行医学断层图像插值时,很多方法并不能兼顾图像灰度和目标形状的变化,且计算过程过于复杂.鉴于此,提出一种基于小波与Lagrange多项式相结合的插值方法.方法 首先对原始图像进行小波变换,获得图像边缘对应小波系数的位置信息,在断层图像的相应小波系数之间运用Lagrange多项式进行强度和位置插值.结果 通过实验验证,采用本文方法插值得到的图像与线性、Cubic插值方法相比,不仅在灰度值不等点方面减少了10%~50%,均方误差平均下降了3%,而且目标组织轮廓特别是拐角剧烈变化处可改善伪轮廓现象,介于原始断层图像之间,能够满足医学图像层间插值的要求.结论 与线性插值方法、Cubic插值方法相比,新算法由于引入了小波变换这个工具,可将图像剧烈变换部分提取出来,因此,本文方法在处理图像剧烈变化的情况时略有优势.新算法得到的插值图像质量有所提高,计算误差有所降低,可有效用于医学图像目标组织的3维重建.     

结构特征下的可撤销人脸识别

目的 相对于其他生物特征识别技术,人脸识别具有非接触、不易察觉和易于推广等特点,在公共安全和日常生活中得到广泛应用。在移动互联网时代,云端人脸识别可以有效地提高识别精度,但是需要将大量的人脸数据上传到第三方服务器。由于人的面部特征是唯一的,一旦数据库泄露就会面临模板攻击和假冒攻击等安全威胁。为了保证人脸识别系统的安全性并提高其识别率,本文提出一种融合人脸结构特征的可撤销人脸识别算法。方法 首先,对原始人脸图像提取结构特征作为虚部分量,与原始人脸图像联合构建复数矩阵并通过随机二值矩阵进行置乱操作。然后,使用2维主成分分析方法将置乱的复数矩阵映射到新的特征空间。最后,采用基于曼哈顿距离的最近邻分类器计算识别率。结果 在4个不同人脸数据库上的实验结果表明,原始人脸图像和结构特征图像经过随机二值矩阵置乱后,人眼无法察觉出有用的信息且可以重新生成,而且融合方差特征后,在GT （Georgia Tech）、NIR （Near Infrared）、VIS （Visible Light）和YMU （YouTuBe Makeup）人脸数据库上,平均人脸识别率分别提高了4.9%、2.25%、2.25%和1.98%,且平均测试时间均在1.0 ms之内,表明该算法实时性强,能够满足实际应用场景的需求。结论 本文算法可在不影响识别率的情况下保证系统的安全性,满足可撤销性。同时,融合结构特征丰富了人脸信息的表征,提高了人脸识别系统的识别率。