Selection of optimized features for fusion of palm print and finger knuckle‐based person authentication

The impact of digital technology in biometrics is much more efficient at interpreting data than humans, which results in completely replacement of manual identification procedures in forensic science. Because the single modality‐based biometric frameworks limit performance in terms of accuracy and anti‐spoofing capabilities due to the presence of low quality data, therefore, information fusion of more than one biometric characteristic in pursuit of high recognition results can be beneficial. In this article, we present a multimodal biometric system based on information fusion of palm print and finger knuckle traits, which are least associated to any criminal investigation as evidence yet. The proposed multimodal biometric system might be useful to identify the suspects in case of physical beating or kidnapping and establish supportive scientific evidences, when no fingerprint or face information is present in photographs. The first step in our work is data preprocessing, in which region of interest of palm and finger knuckle images have been extracted. To minimize nonuniform illumination effects, we first normalize the detected circular palm or finger knuckle and then apply line ordinal pattern (LOP)‐based encoding scheme for texture enrichment. The nondecimated quaternion wavelet provides denser feature representation at multiple scales and orientations when extracted over proposed LOP encoding and increases the discrimination power of line and ridge features. To best of our knowledge, this first attempt is a combination of backtracking search algorithm and 2D²LDA has been employed to select the dominant palm and knuckle features for classification. The classifiers output for two modalities are combined at unsupervised rank level fusion rule through Borda count method, which shows an increase in performance in terms of recognition and verification, that is, 100% (correct recognition rate), 0.26% (equal error rate), 3.52 (discriminative index), and 1,262 m (speed).     

Multimodal biometrics: Weighted score level fusion based on non-ideal iris and face images

The iris and face are among the most promising biometric traits that can accurately identify a person because their unique textures can be swiftly extracted during the recognition process. However, unimodal biometrics have limited usage since no single biometric is sufficiently robust and accurate in real-world applications. Iris and face biometric authentication often deals with non-ideal scenarios such as off-angles, reflections, expression changes, variations in posing, or blurred images. These limitations imposed by unimodal biometrics can be overcome by incorporating multimodal biometrics. Therefore, this paper presents a method that combines face and iris biometric traits with the weighted score level fusion technique to flexibly fuse the matching scores from these two modalities based on their weight availability. The dataset use for the experiment is self established dataset named Universiti Teknologi Malaysia Iris and Face Multimodal Datasets (UTMIFM), UBIRIS version 2.0 (UBIRIS v.2) and ORL face databases. The proposed framework achieve high accuracy, and had a high decidability index which significantly separate the distance between intra and inter distance.     

Situation awareness of cancelable biometric system

Recently, cancelable biometrics emerged as one of the highly effective methods of template protection. The concept behind the cancelable biometrics or cancelability is a transformation of a biometric data or extracted feature into an alternative form, which cannot be used by the imposter or intruder easily, and can be revoked if compromised. In this paper, we present a novel architecture for template generation in the context of situation awareness system in real and virtual applications. We develop a novel cancelable biometric template generation algorithm utilizing random biometric fusion, random projection and selection. Proposed random cross-folding method generate cancelable biometric template from multiple biometric traits. We further validate the performance of the proposed algorithm using a virtual multimodal face and ear database.     

A review on performance,security and various biometric template protection schemes for biometric authentication systems

Identifying a person based on their behavioral and biological qualities in an automated manner is called biometrics. The authentication system substituting traditional password and token for authentication and relies gradually on biometric authentication methods for verification of the identity of an individual. This proves the fact that society has started depending on biometric-based authentication systems. Security of biometric authentication needs to be reviewed and discussed as there are multiple points related to integrity and public reception of biometric-based authentication systems. Security and recognition accuracy are the two most important aspects which must be considered while designing biometric authentication systems. During enrollment phase scanning of biometric data is done to determine a set of distinct biometric feature set known as biometric template. Protection of biometric templates from various hacking efforts is a topic of vital importance as unlike passwords or tokens, compromised biometric templates cannot be reissued. Therefore, giving powerful protection techniques for biometric templates and still at that very moment preparing great identification accuracy is a good research problem nowadays, as well as in the future. Furthermore, efficiency under non-ideal conditions is also supposed to be inadequate and thus needs special attention in the design of a biometric authentication system. Disclosure of various biometric traits in miscellaneous applications creates a severe compromise on the privacy of the user. Biometric authentication can be utilized for remote user authentication. In this case, the biometric data of users typically called templates are stored in a server. The uniqueness and stability of biometrics ended it useful over traditional authentication systems. But, a similar thing made the enduring harm of a user’s identity in biometric systems. The architecture of the biometric system leads to several hazards that lead to numerous security concerns and privacy threats. To address this issue, biometric templates are secured using several schemes that are categorized as biometric cryptosystems, cancelable biometrics, hybrid methods, Homomorphic Encryption, visual cryptography based methods. Biometric cryptosystems and cancelable biometrics techniques provide reliable biometric security at a great level. However, there persist numerous concerns and encounters that are being faced during the deployment of these protection technologies. This paper reviews and analyses various biometric template protection methods. This review paper also reflects the limitations of various biometric template protection methods being used in present times and highlights the scope of future work.

     

Automated Teller Machine Authentication Using Biometric

This paper presents a novel method of a secured card-less Automated Teller Machine (ATM) authentication based on the three bio-metrics measures. It would help in the identification and authorization of individuals and would provide robust security enhancement. Moreover, it would assist in providing identification in ways that cannot be impersonated. To the best of our knowledge, this method of Biometric_ fusion way is the first ATM security algorithm that utilizes a fusion of three biometric features of an individual such as Fingerprint, Face, and Retina simultaneously for recognition and authentication. These biometric images have been collected as input data for each module in this system, like a fingerprint, a face, and a retina module. A database is created by converting these images to YIQ color space, which is helpful in normalizing the brightness levels of the image hence mainly (Y component’s) luminance. Then, it attempt to enhance Cellular Automata Segmentation has been carried out to segment the particular regions of interest from these database images. After obtaining segmentation results, the featured extraction method is carried out from these critical segments of biometric photos. The Enhanced Discrete Wavelet Transform technique (DWT Mexican Hat Wavelet) was used to extract the features. Fusion of extracted features of all three biometrics features have been used to bring in the multimodal classification approach to get fusion vectors. Once fusion vectors ware formulated, the feature level fusion technique is incorporated based on the extracted feature vectors. These features have been applied to the machine learning algorithm to identify and authorization of multimodal biometrics for ATM security. In the proposed approach, we attempt at useing an enhanced Deep Convolutional Neural Network (DCNN). A hybrid optimization algorithm has been selected based on the effectiveness of the features. The proposed approach results were compared with existing algorithms based on the classification accuracy to prove the effectiveness of our algorithm. Moreover, comparative results of the proposed method stand as a proof of more promising outcomes by combining the three biometric features.     

多模态生物特征提取及相关性评价综述

生物特征识别是身份认证的重要手段,特征提取技术在其中扮演了关键角色,直接影响识别的结果。随着特征提取技术日趋成熟,学者们逐渐将目光投向了生物特征间的相关性问题。本文以单模态和多模态生物识别中的特征提取方法为研究对象,回顾了人脸与指纹的特征提取方法,分析了基于经验知识的特征分类提取方法以及基于深度学习的计算机逻辑采样提取方法,并从图像处理的角度对单模态与多模态方法进行对比。以当前多模态生物特征提取方法和DNA表达过程为引,提出了不同模态的生物特征之间存在相关性的猜想,以及对这一猜想进行建模的思路。在多模态生物特征提取的基础上,对今后可能有进展的各生物特征之间的相关性建模进行了展望。     

Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems

We examine the performance of multimodal biometric authentication systems using state-of-the-art commercial off-the-shelf (COTS) fingerprint and face biometric systems on a population approaching 1,000 individuals. The majority of prior studies of multimodal biometrics have been limited to relatively low accuracy non-COTS systems and populations of a few hundred users. Our work is the first to demonstrate that multimodal fingerprint and face biometric systems can achieve significant accuracy gains over either biometric alone, even when using highly accurate COTS systems on a relatively large-scale population. In addition to examining well-known multimodal methods, we introduce new methods of normalization and fusion that further improve the accuracy.     

FEATURES-LEVEL FUSION OF FACE AND HANDWRITTEN SIGNATURE IN MULTIMODAL BIOMETRIC IDENTIFICATION SYSTEM

Biometrics is an emerging tool used to identify humans by their physical and/or behavioral characteristics. This article presents a novel neural network–based approach for features-level fusion in a multimodal biometric identification system by combining both physical (human face) and behavioral (handwritten signature) traits. A single biometrics system has the weakness of providing neither 100% identification nor a 0% false accept rate (FAR)/false reject rate (FRR). One solution to this is to combine different biometrics together to get a multimodal biometric identification system. Moreover, a multimodal system is also robust in providing security against spoof attacks. Images of 32 × 32 pixels are used to eliminate bulk storage and processing requirements.     

Feature-level fusion of fingerprint and finger-vein for personal identification

Multimodal biometrics based on feature-level fusion is a significant topic in personal identification research community. In this paper, a new fingerprint-vein based biometric method is proposed for making a finger more universal in biometrics. The fingerprint and finger-vein features are first exploited and extracted using a unified Gabor filter framework. Then, a novel supervised local-preserving canonical correlation analysis method (SLPCCAM) is proposed to generate fingerprint-vein feature vectors (FPVFVs) in feature-level fusion. Based on FPVFVs, the nearest neighborhood classifier is employed for personal identification finally. Experimental results show that the proposed approach has a high capability in fingerprint-vein based personal recognition as well as multimodal feature-level fusion.     

Face Templates Encryption Technique Based on Random Projection and Deep Learning

Cancellable biometrics is the solution for the trade-off between two concepts: Biometrics for Security and Security for Biometrics. The cancelable template is stored in the authentication system’s database rather than the original biometric data. In case of the database is compromised, it is easy for the template to be canceled and regenerated from the same biometric data. Recoverability of the cancelable template comes from the diversity of the cancelable transformation parameters (cancelable key). Therefore, the cancelable key must be secret to be used in the system authentication process as a second authentication factor in conjunction with the biometric data. The main contribution of this paper is to tackle the risks of stolen/lost/shared cancelable keys by using biometric trait (in different feature domains) as the only authentication factor, in addition to achieving good performance with high security. The standard Generative Adversarial Network (GAN) is proposed as an encryption tool that needs the cancelable key during the training phase, and the testing phase depends only on the biometric trait. Additionally, random projection transformation is employed to increase the proposed system’s security and performance. The proposed transformation system is tested using the standard ORL face database, and the experiments are done by applying different features domains. Moreover, a security analysis for the proposed transformation system is presented.     

基于指部关联特征的多模态图像采集系统

针对基于单个生物特征的身份认证安全性和稳定性不足的问题,设计了基于指部关联特征的多模态图像采集系统,采用单个双波段摄像头分时采集同一根手指的指纹、指节纹和指静脉图像。指纹和指节纹采用非接触反射采集方式,指静脉采用单侧近红外光源与反射镜面相结合的透射采集方式,并根据静脉图像质量评价动态调控光源,根据特征点信息量动态调整各个特征的权重。实验结果表明,该多模态采集系统在认证通过率、误识率和拒登率等指标都优于指纹或指静脉的单模态采集系统,认证通过率达到99.1%,误识率为0.000 1%,不存在拒登现象。     

A feature extraction method for use with bimodal biometrics

Bimodal biometrics has been found to outperform single biometrics and are usually implemented using the matching score level or decision level fusion, though this fusion will enable less information of bimodal biometric traits to be exploited for personal authentication than fusion at the feature level. This paper proposes matrix-based complex PCA (MCPCA), a feature level fusion method for bimodal biometrics that uses a complex matrix to denote two biometric traits from one subject. The method respectively takes the two images from two biometric traits of a subject as the real part and imaginary part of a complex matrix. MCPCA applies a novel and mathematically tractable algorithm for extracting features directly from complex matrices. We also show that MCPCA has a sound theoretical foundation and the previous matrix-based PCA technique, two-dimensional PCA (2DPCA), is only one special form of the proposed method. On the other hand, the features extracted by the developed method may have a large number of data items (each real number in the obtained features is called one data item). In order to obtain features with a small number of data items, we have devised a two-step feature extraction scheme. Our experiments show that the proposed two-step feature extraction scheme can achieve a higher classification accuracy than the 2DPCA and PCA techniques.     

基于手指双模态特征的自动身份验证方法及系统

针对目前单模态生物特征识别在稳定性与安全性等方面的不足以及多模态融合识别的多设备多输入困难等问题, 本文提出一种充分考虑类内与类间度量的学习模型, 实现基于手指双模态特征的自动身份验证方法及系统。由于指静脉与指折痕具有不易改变, 难以伪造的特点, 本文选取这两种重要的手部特征进行身份验证。通过结合两种不同模态特征, 利用自编码网络对类内特征进行表示, 来构建基于度量学习的孪生网络模型, 从而提取类内与类间特征; 接着将提取的指静脉和指折痕特征进行距离计算, 将距离融合后使用逻辑回归模型进行概率判断, 最终实现有效的双模态融合身份验证。为验证我们提出方法的有效性,我们对指静脉识别结果性能进行了对比。实验结果表明, 我们的方法在更具有挑战性的数据库上识别等错误率为 1.69%, 较之现有代表性论文提出的模型的等错误率降低了 2.96%。我们也将构建的双模态融合模型与仅使用单一模态模型进行对比, 结果表明融合指静脉和指折痕特征的融合模型的等错误率为 1.55%,比单一模态的指静脉与指折痕模型分别降低了 0.14%和 3.0%, 表明了双模态身份验证模型性能更优。进一步地, 本文采集了一个更具有挑战性的数据库, 开发了显示图像及识别结果的图形界面,最终实现了一个从数据采集到识别匹配的端对端的一体化自动身份验证系统。基于以上研究, 本文首次提出了一个基于指静脉和指折痕特征的多目自动身份验证方案, 实现集准确性, 鲁棒性和实效性为一体的系统。     

Graphics and security: exploring visual biometrics

Biometrics technology has come a long way from simpler forms of systems security. But are biometrics-based systems more secure or do they simply require crackers to become more proficient at breaking into systems? To recognize your fingerprint requires that a template of your fingerprint actually be present in the system that verifies your access. If you want to pass as somebody else, presumably you'd have to either have that person's finger with you or you'd need to change the verifying template residing in the system that verifies your print. Cracking into a system and replacing a legitimate print with your own isn't easy to do unless the system's security is poor. While biometric proponents stress the strength of their proprietary technologies or biometrics in general, no system is ever completely secure. Contrary to what many biometric proponents would have us believe-that biometric security outclasses traditional forms of security-all biometric systems are, after all, another form of computer security with its own set of strengths and weaknesses. Biometrics effectively trade some amount of privacy and cost effectiveness for ultimate convenience-and these systems are certainly no less secure than standard password systems. Password systems are cheap. Complex biometric scanning equipment is usually expensive. But biometrics seems to be where the industry is headed.     

结合人脸特征和密码技术的网络身份认证系统*

为了解决传统密码或单纯生物特征在远程身份认证中存在的安全问题,利用人脸识别中特征模板的生成特点,设计了一种人脸特征与密码技术相结合的双重身份认证方案。人脸识别的仿真结果证明了人脸特征用于识别的有效性;系统分析表明,该系统能够对付传统网络身份认证中所遇到的窃听、重放、假冒或窜改等各种攻击,且实时性比较好。     

Securing Fingerprint Images Using PSO-Based Wavelet Domain Watermarking

ABSTRACT

Watermarking techniques are used in biometric systems for the purpose of protecting and authenticating biometric data. This paper presents an efficient scheme to protect and authenticate fingerprint images by watermarking with their corresponding facial images in the wavelet domain using Particle Swarm Optimization (PSO). The key idea is to use PSO to find the best discrete wavelet transform (DWT) coefficients where the facial image data can be embedded. The objective function for PSO is based on the fingerprint image quality with respect to the Structural Similarity index (SSIM) and Orientation Certainty Level index (OCL). As a result, embedding the facial image data in the selected coefficients generated by the proposed method not only results in minimum distortion of the host image but also retains the feature set of the original fingerprint to be used in fingerprint recognition. The robustness of the watermark extracted using the proposed technique has been tested against various image processing attacks. This concept of watermarking a biometric image with another biometric image finds application in multimodal biometric authentication for a more secure system of personal recognition at the receiver's end.     

Privacy preserving security using biometrics in cloud computing

Cloud computing and the efficient storage provide new paradigms and approaches designed at efficiently utilization of resources through computation and many alternatives to guarantee the privacy preservation of individual user. It also ensures the integrity of stored cloud data, and processing of stored data in the various data centers. However, to provide better protection and management of sensitive information (data) are big challenge to maintain the confidentiality and integrity of data in the cloud computation. Thus, there is an urgent need for storing and processing the data in the cloud environment without any information leakage. The sensitive data require the storing and processing mechanism and techniques to assurance the privacy preservation of individual user, to maintain the data integrity, and preserve confidentiality. Face recognition has recently achieved advancements in the unobtrusive recognition of individuals to maintain the privacy-preservation in the cloud computing. This paper emphasizes on cloud security and privacy issues and provides the solution using biometric face recognition. We propose a biometrics face recognition approach for security and privacy preservation of cloud users during their access to cloud resources. The proposed approach has three steps: (1) acquisition of face images (2) preprocessing and extraction of facial feature (3) recognition of individual using encrypted biometric feature. The experimental results establish that our proposed recognition approach can ensure the privacy and security of biometrics data.