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.     

基于生物特征和口令放大的远程认证协议

基于口令的认证协议具有简单、方便、强适应性及移动性等优点,它广泛应用于网上银行、ATM等远程登录环境中。但是一般用户的口令具有低熵、安全性低、口令数据难以保护等缺点,从而使系统存在许多安全隐患。口令放大是这样的一种算法,它输入用户的低熵的口令和一个高熵的随机数,然后输出一个高熵的新口令,从而提高了系统的安全性,也不增加用户的负担。人体生物特征是人体所固有的生理和行为特征,而模糊提取器可以从人体的生物特征中提取出高熵的随机串。生物特征和口令放大的结合,恰好可以克服基于口令的认证协议的缺点,提高其安全性。提出了一种结合人体生物特征和口令放大的单向认证协议,充分发挥了基于口令的认证协议所具有的简单易用和生物特征高熵、安全性高等优点,并且具有一定的容错能力。     

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.     

Face and palmprint pixel level fusion and Kernel DCV-RBF classifier for small sample biometric recognition

Recently, multi-modal biometric fusion techniques have attracted increasing atove the recognition performance in some difficult biometric problems. The small sample biometric recognition problem is such a research difficulty in real-world applications. So far, most research work on fusion techniques has been done at the highest fusion level, i.e. the decision level. In this paper, we propose a novel fusion approach at the lowest level, i.e. the image pixel level. We first combine two kinds of biometrics: the face feature, which is a representative of contactless biometric, and the palmprint feature, which is a typical contacting biometric. We perform the Gabor transform on face and palmprint images and combine them at the pixel level. The correlation analysis shows that there is very small correlation between their normalized Gabor-transformed images. This paper also presents a novel classifier, KDCV-RBF, to classify the fused biometric images. It extracts the image discriminative features using a Kernel discriminative common vectors (KDCV) approach and classifies the features by using the radial base function (RBF) network. As the test data, we take two largest public face databases (AR and FERET) and a large palmprint database. The experimental results demonstrate that the proposed biometric fusion recognition approach is a rather effective solution for the small sample recognition problem.     

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.     

Bio-PUF-MAC authenticated encryption for iris biometrics

Biometrics is one of the ways for human authentication. Fabrication of biometrics by intruders, limits the accuracy of authentication. The user-specific keys (ie,) pseudo-random numbers give more security for biometric template protection and increase the accuracy of authentication also. The user-specific token or keys can also be fabricated by intruders by any of the prediction methods. To avoid the creation of fake biometric and fake user-specific keys, a device-specific Physical Unclonable Function (PUF) is proposed. In this article, iris authentication is provided by unclonable PUF-based true random numbers to enhance the unique authentication. Nonreversible Message Authentication Codes (MAC) are developed using PUF and Discrete Wavelet Transform features of iris biometrics. Systematically, MAC codes also created with, encryption algorithm. Encryption is additionally providing confidentiality in the individual iris. Experiments are done with CUHK Iris Image Dataset. Proposed Bio-PUF system has significant functional advantages in point of view of the unclonable pseudo-random number from PUF. Experimentally, Avalanche effect, entropy, NCPR, and UACI parameters are analyzed with PUF-based crypt functions. For 75% of matching with the Bio-PUF-MAC codes with enrolment, the accuracy for correct identification is 77.73%.     

基于比特流的虹膜特征数据的隐藏算法

对生物特征数据的攻击是生物特征识别自身安全的主要威胁。为了提高虹膜特征数据的安全性,根据现有主要的虹膜识别方法中特征模板的数据特性和基于汉明距的比对方法,提出一种基于比特流的将虹膜特征模板数据嵌入人脸图像的数据隐藏算法。实验结果表明,该算法具有较强的隐蔽性,隐藏算法本身误码率为零,计算效率高,不会影响虹膜识别技术本身的性能,能够有效保护特征模板数据,增强虹膜识别系统自身的安全性。     

生物特征识别综述*

对传统的身份鉴别手段和现代生物特征识别技术产生背景进行了综述,介绍目前典型的生物特征识别技术的原理和发展趋势,分析了实际应用中存在的优缺点,并探讨了基于个人生物特征融合的多生物特征识别技术和生物特征信息保护问题,强调了生物识别技术在身份认证领域的重要性。     

Secure Hamming distance computation for biometrics using ideal-lattice and ring-LWE homomorphic encryption

With widespread development of biometrics, concerns about security and privacy are rapidly increasing. Homomorphic encryption enables us to operate on encrypted data without decryption, and it can be applied to construct a privacy-preserving biometric system. In this article, we apply two homomorphic encryption schemes based on ideal-lattice and ring-LWE (Learning with Errors), which both have homomorphic correctness over the ring of integers of a cyclotomic field. We compare the two schemes in applying them to privacy-preserving biometrics. In biometrics, the Hamming distance is used as a metric to compare two biometric feature vectors for authentication. We propose an efficient method for secure Hamming distance. Our method can pack a biometric feature vector into a single ciphertext, and it enables efficient computation of secure Hamming distance over our packed ciphertexts.     

Cancellable biometrics and annotations on BioHash

Lately, the once powerful one-factor authentication which is based solely on either password, token or biometric approach, appears to be insufficient in addressing the challenges of identity frauds. For example, the sole biometric approach suffers from the privacy invasion and non-revocable issues. Passwords and tokens are easily forgotten and lost. To address these issues, the notion of cancellable biometrics was introduced to denote biometric templates that can be cancelled and replaced with the inclusion of another independent authentication factor. BioHash is a form of cancellable biometrics which mixes a set of user-specific random vectors with biometric features. In verification setting, BioHash is able to deliver extremely low error rates as compared to the sole biometric approach when a genuine token is used. However, this raises the possibility of two identity theft scenarios: (i) stolen-biometrics, in which an impostor possesses intercepted biometric data of sufficient high quality to be considered genuine and (ii) stolen-token, in which an impostor has access to the genuine token and used by the impostor to claim as the genuine user. We found that the recognition rate for the latter case is poorer. In this paper, the quantised random projection ensemble based on the Johnson–Lindenstrauss Lemma is used to establish the mathematical foundation of BioHash. Based on this model, we elucidate the characteristics of BioHash in pattern recognition as well as security view points and propose new methods to rectify the stolen-token problem.     

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.     

Cancelable features using log-Gabor filters for biometric authentication

Wide spread use of biometric based authentication requires security of biometric data against identity thefts. Cancelable biometrics is a recent approach to address the concerns regarding privacy of biometric data, public confidence, and acceptance of biometric systems. This work proposes a template protection approach which generates revocable binary features from phase and magnitude patterns of log-Gabor filters. Multi-level transformations are applied at signal and feature level to distort the biometric data using user specific tokenized variables which are observed to provide better performance and security against information leakage under correlation attacks. A thorough analysis is performed to study the performance, non-invertibility, and changeability of the proposed approach under stolen token scenario on multiple biometric modalities. It is revealed that generated templates are non-invertible, easy to revoke, and also deliver good performance.     

基于生物识别的网络身份认证新方案

文章分析了现今基于生物识别技术的网络认证没被广泛应用的原因：生物特征的提取一般需要特殊的专用设备、指纹的利用比较泛滥、生物特征遗失后挂失比较困难等。文章同时对比了几种比较主流的生物特征识别技术,分析了它们各自的实用性、便捷性以及安全性,指出生物3D打印技术在未来对生物识别技术带来冲击的可能。文章提出了一种基于动态人脸识别的网络认证方案,该方案利用人脸作为网络认证的基础,通过跟踪实时人脸活动来实现实时人脸图像的采集,预防了照片攻击和视频攻击,提高了认证的可靠性和安全性。文章最后通过分析该方案的可实现性、可叠加性和安全性,并从成本等方面考虑,得出该方案性能较优的结论,同时对生物识别技术应用于网络认证进行了展望。由于生物特征具有唯一性和不可重置的特点,所以生物特征保护需要引起更高的关注,也需要更多学者做相关的研究,更好地利用生物特征。     

基于模糊循环随机映射的人脸生物特征加密算法

随着生物特征识别技术的广泛应用,其安全性方面的缺陷也逐渐暴露出来。密码技术与生物特征识别技术相结合的生物特征加密技术,就是为了弥补生物特征识别在安全方面的不足而产生的。在研究已有人脸生物特征识别技术的基础上,提出一种兼具安全性及容错能力的人脸生物特征加密算法:模糊循环随机映射(Fuzzy Cyclic Random Mapping,FCRM)。在每次循环中,加密模型使用前一次循环的密钥作为随机种子生成映射矩阵,对用户的人脸特征进行映射,形成一个循环的随机映射过程。加密过程中,还使用了容错技术来减少合法用户人脸图像和特征的随机噪声对识别率的影响,而循环的映射过程能够在不减少认证准确率的前提下,阻止非法用户通过认证。     

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.     

Multimodal biometric cryptosystem for human authentication using fingerprint and ear

The multimodal biometrics is mainly used for the purpose of person certification and proof. Lot of biometrics is used for human authentication. In which ear and fingerprint are efficient one. There are three vital phases involved in the biometric detection which include the Preprocessing, Feature extraction and the classification. Initially, preprocessing is done with the help of median filter which lends a helping hand to the task of cropping the image for choosing the position. Then, from the preprocessed Finger print and ear image texture and shape features are extracted. In the long run, the extracted features are integrated. The integrated features, in turn, are proficiently classified by means of the optimal neural network (ONN). Here, the NN weights are optimally, selected with the help of firefly algorithm (FF). The biometric image is classified into fingerprint and ear if the identical person images are amassed in one group and the uneven images are stored in a different group. The performance of the proposed approach is analyzed in terms of evaluation metrics.

     

Single-sensor hand-vein multimodal biometric recognition using multiscale deep pyramidal approach

Biometrics has emerged as a powerful technology for person authentication in various scenarios including forensic and civilian applications. Deployment of biometric solutions that use cues from multiple modalities enhances the reliability and robustness of authentication necessary to meet the increasingly stringent security requirements. However, there are two drawbacks typically associated with multimodal biometrics. Firstly, the image acquisition process in such systems is not very user-friendly, primarily due to the time and effort required to capture biometric samples belonging to multiple modalities. Secondly, the overall cost is higher as they employ multiple biometric sensors. To overcome these drawbacks, we employ a single NIR sensor-based image acquisition in the proposed approach for hand-vein recognition. From the input hand image, a palm-vein and four finger-vein subimages are extracted. These images are then enhanced by CLAHE and transformed into illumination invariant representation using center-symmetric local binary pattern (CS-LBP). Further, a hierarchical non-rigid matching technique inspired by the architecture of deep convolutional networks is employed for matching the CS-LBP features. Finally, weighted sum rule-based matching score-level fusion is performed to combine the palm-vein and the four finger-vein modalities. A set of rigorous experiments has been performed on an in-house database collected from the left and right hands of 185 subjects and the publicly available CASIA dataset. The proposed approach achieves equal error rates of 0.13% and 1.21%, and rank-1 identification rates of 100% and 100% on the in-house and CASIA datasets, respectively. Additionally, we compare the proposed approach with the state-of-the-art techniques proposed for vascular biometric recognition in the literature. The important findings are (1) the proposed approach outperforms all the existing techniques considered in this study, (2) the fusion of palm-vein and finger-vein modalities consistently leads to better performance for all the feature extraction techniques considered in this work. (3) Furthermore, our experimental results also suggest that considering the constituent palm-vein and finger-vein images instead of the entire hand-vein images achieves better performance.     

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.

     

Evaluation of a template protection approach to integrate fingerprint biometrics in a PIN-based payment infrastructure

Biometric authentication has a great potential to improve the security, reduce cost, and enhance the customer convenience of payment systems. Despite these benefits, biometric authentication has not yet been adopted by large-scale point-of-sale and automated teller machine systems. This paper aims at providing a better understanding of the benefits and limitations associated with the integration of biometrics in a PIN-based payment authentication system. Based on a review of the market drivers and deployment hurdles, a method is proposed in which biometrics can be seamlessly integrated in a PIN-based authentication infrastructure. By binding a fixed binary, renewable string to a noisy biometric sample, the data privacy and interoperability between issuing and acquiring banks can improve considerably compared to conventional biometric approaches. The biometric system security, cost aspects, and customer convenience are subsequently compared to PIN by means of simulations using fingerprints. The results indicate that the biometric authentication performance is not negatively influenced by the incorporation of key binding and release processes, and that the security expressed as guessing entropy of the biometric key is virtually identical to the current PIN. The data also suggest that for the fingerprint database under test, the claimed benefits for cost reduction, improved security and customer convenience do not convincingly materialize when compared to PIN. This result can in part explain why large-scale biometric payment systems are virtually non-existent in Europe and the United States, and suggests that other biometric modalities than fingerprints may be more appropriate for payment systems.     

Secure-OSCAR中基于PKI的生物身份认证的设计

个体的生物特征的唯一性和“不可伪造性”使得它很适合于身份认证。生物信息本来是不保密的,所以不能象使用口令一样来使用它,否则将不能提高反而会降低系统的安全性。公钥机制(PKI)也被广泛应用于用户身份认证中,但它是基于私钥的安全性的,不可避免地存在冒用私钥的威胁。论文提出一个结合生物技术与PKI技术的认证方式的设计,具体描述了它在Secure-OSCAR中的实现。