Investigating fusion approaches in multi-biometric cancellable recognition

Cancellable biometrics has recently been introduced in order to overcome some privacy issues about the management of biometric data, aiming to transform a biometric trait into a new but revocable representation for enrolment and identification (verification). Therefore, a new representation of original biometric data can be generated in case of being compromised. Additionally, the use multi-biometric systems are increasingly being deployed in various biometric-based applications since the limitations imposed by a single biometric model can be overcome by these multi-biometric recognition systems. In this paper, we specifically investigate the performance of different fusion approaches in the context of multi-biometrics cancellable recognition. In this investigation, we adjust the ensemble structure to be used for a biometric system and we use as examples two different biometric modalities (voice and iris data) in a multi-biometrics context, adapting three cancellable transformations for each biometric modality.     

A New Method for Generating an Invariant Iris Private Key Based on the Fuzzy Vault System

Cryptographic systems have been widely used in many information security applications. One main challenge that these systems have faced has been how to protect private keys from attackers. Recently, biometric cryptosystems have been introduced as a reliable way of concealing private keys by using biometric data. A fuzzy vault refers to a biometric cryptosystem that can be used to effectively protect private keys and to release them only when legitimate users enter their biometric data. In biometric systems, a critical problem is storing biometric templates in a database. However, fuzzy vault systems do not need to directly store these templates since they are combined with private keys by using cryptography. Previous fuzzy vault systems were designed by using fingerprint, face, and so on. However, there has been no attempt to implement a fuzzy vault system that used an iris. In biometric applications, it is widely known that an iris can discriminate between persons better than other biometric modalities. In this paper, we propose a reliable fuzzy vault system based on local iris features. We extracted multiple iris features from multiple local regions in a given iris image, and the exact values of the unordered set were then produced using the clustering method. To align the iris templates with the new input iris data, a shift-matching technique was applied. Experimental results showed that 128-bit private keys were securely and robustly generated by using any given iris data without requiring prealignment.      

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.     

Palmprint Verification for Controlling Access to Shared Computing Resources

You can build an effective palmprint verification system using a combination of mostly off-the-shelf components and techniques. Access security is an important aspect of pervasive computing systems. It offers the system developer and end users a certain degree of trust in the use of shared computing resources. Biometrics verification offers many advantages over the username-plus-password approach for access control. Users don't have to memorize any codes or passwords, and biometric systems are more reliable because biometric characteristics can't easily be duplicated, lost, or stolen. Researchers have studied such biometric characteristics as faces, fingerprints, irises, voices, and palmprints.Facial appearance and features change with age. Fingerprints can be affected by surface abrasions or otherwise compromised. Capturing iris images is relatively difficult, and iris scans can be intrusive. Voices are susceptible to noise corruption and can be easily copied and manipulated. Palmprints are potentially a good choice for biometric applications because they're invariant with a person, easy to capture, and difficult to duplicate. They offer greater security than fingerprints because palm veins are more complex than finger veins. However, compared to other biometric characteristics, they have perhaps seen less research. This provides a big opportunity for advancing palmprint technology and applications. We've developed an effective prototype palmprint verification system using a combination of mostly off-the-shelf (and therefore tried and tested) components and techniques. Such an approach should make palmprint verification an appealing proposition.     

Personal identification based on iris texture analysis

With an increasing emphasis on security, automated personal identification based on biometrics has been receiving extensive attention over the past decade. Iris recognition, as an emerging biometric recognition approach, is becoming a very active topic in both research and practical applications. In general, a typical iris recognition system includes iris imaging, iris liveness detection, and recognition. This paper focuses on the last issue and describes a new scheme for iris recognition from an image sequence. We first assess the quality of each image in the input sequence and select a clear iris image from such a sequence for subsequent recognition. A bank of spatial filters, whose kernels are suitable for iris recognition, is then used to capture local characteristics of the iris so as to produce discriminating texture features. Experimental results show that the proposed method has an encouraging performance. In particular, a comparative study of existing methods for iris recognition is conducted on an iris image database including 2,255 sequences from 213 subjects. Conclusions based on such a comparison using a nonparametric statistical method (the bootstrap) provide useful information for further research.     

A personal identification system using retinal vasculature in retinal fundus images

The characteristics of human body such as fingerprint, face, hand palm and iris are measured, recorded and identified by performing comparison using biometric devices. Even though it has not seen widespread acceptance yet, retinal identification based on retinal vasculatures in retina provides the most secure and accurate authentication means among biometric systems. Using retinal images taken from individuals, retinal identification is employed in environments such as nuclear research centers and facilities, weapon factories, where extremely high security measures are needed. The superiority of this method stems from the fact that retina is unique to every human being and it would not be changed during human life. Adversely, other identification approaches such as fingerprint, face, palm and iris recognition, are all vulnerable in that those characteristics can be corrupted via plastic surgeries and other changes. In this study we propose an alternate personal identification system based on retinal vascular network in retinal images, which tolerates scale, rotation and translation in comparison. In order to accurately identify a person our new approach first segments vessel structure and then employ similarity measurement along with the tolerations. The developed system, tested on about four hundred images, presents over 95% of success which is quite promising.     

FIRME: Face and Iris Recognition for Mobile Engagement

Mobile devices, namely phones and tablets, have long gone “smart”. Their growing use is both a cause and an effect of their technological advancement. Among the others, their increasing ability to store and exchange sensitive information, has caused interest in exploiting their vulnerabilities, and the opposite need to protect users and their data through secure protocols for access and identification on mobile platforms. Face and iris recognition are especially attractive, since they are sufficiently reliable, and just require the webcam normally equipping the involved devices. On the contrary, the alternative use of fingerprints requires a dedicated sensor. Moreover, some kinds of biometrics lend themselves to uses that go beyond security. Ambient intelligence services bound to the recognition of a user, as well as social applications, such as automatic photo tagging on social networks, can especially exploit face recognition. This paper describes FIRME (Face and Iris Recognition for Mobile Engagement) as a biometric application based on a multimodal recognition of face and iris, which is designed to be embedded in mobile devices. Both design and implementation of FIRME rely on a modular architecture, whose workflow includes separate and replaceable packages. The starting one handles image acquisition. From this point, different branches perform detection, segmentation, feature extraction, and matching for face and iris separately. As for face, an antispoofing step is also performed after segmentation. Finally, results from the two branches are fused. In order to address also security-critical applications, FIRME can perform continuous reidentification and best sample selection. To further address the possible limited resources of mobile devices, all algorithms are optimized to be low-demanding and computation-light.     

Anonymous subject identification and privacy information management in video surveillance

The widespread deployment of surveillance cameras has raised serious privacy concerns, and many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. Of equal importance are the privacy and efficiency of techniques to first, identify those individuals for privacy protection and second, provide access to original surveillance video contents for security analysis. In this paper, we propose an anonymous subject identification and privacy data management system to be used in privacy-aware video surveillance. The anonymous subject identification system uses iris patterns to identify individuals for privacy protection. Anonymity of the iris-matching process is guaranteed through the use of a garbled-circuit (GC)-based iris matching protocol. A novel GC complexity reduction scheme is proposed by simplifying the iris masking process in the protocol. A user-centric privacy information management system is also proposed that allows subjects to anonymously access their privacy information via their iris patterns. The system is composed of two encrypted-domain protocols: The privacy information encryption protocol encrypts the original video records using the iris pattern acquired during the subject identification phase; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of our framework.     

煤矿入井人员唯一性检测技术研究

通过分析掌纹、指纹、虹膜、人脸、步态、声纹等生物特征识别技术的特点以及煤矿现场对入井人员生物特征的影响,指出虹膜识别、人脸识别、步态识别、声纹识别适用于煤矿入井人员唯一性检测;提出了一种基于人员定位和生物特征识别的煤矿入井人员唯一性检测技术方案,将生物特征识别技术嵌入人员定位系统,利用人员定位识别卡实现识别卡数量及人员身份的唯一性检测;指出煤矿入井人员唯一性检测技术的研究关键点是严重污染人脸的识别算法、对设备遮挡情况下人员步态图像的采集及对混入人员语音信号的煤矿现场噪声消除算法。     

一种新的基于小波过零检测的虹膜识别算法*

虹膜识别被认为是目前最准确可靠的生物特征识别方法。提出了一种新的基于小波过零检测的虹膜识别算法,通过对分离的虹膜纹理采用小波变换来实现特征提取,最后通过Hamming距离完成模式匹配。实验结果表明,该算法识别率很高,可有效地应用于身份鉴别系统中。     

基于相位谱互相关的虹膜识别方法研究

利用虹膜图像中丰富的结构和纹理特征作为身份鉴别的依据，与其他生物特征识别相比，具有更高的可靠性。该文提出一种基于图像相位谱互相关的虹膜识别方法。本系统先对虹膜图像进行滤波、定位、增强等预处理，再利用相位谱互相关匹配算法对虹膜图像进行识别，最后，进行了小样本的实验。实验结果表明，该方法是可行的，并且识别准确率高，速度快。     

Genetically identical irises have texture similarity that is not detected by iris biometrics

As the standard iris biometric algorithm “sees” them, the left and right irises of the same person are as different as irises of unrelated people. Similarly, in terms of iris biometric matching, the eyes of identical twins are as different as irises of unrelated people. The left and right eyes of an individual or the eyes of identical twins are examples of genetically identical irises. In experiments with human observers viewing pairs of iris images acquired using an iris biometric system, we have found that there is recognizable similarity in the left and right irises of an individual and in the irises of identical twins. This result suggests that iris texture analysis different from that performed in the standard iris biometric algorithm may be able to answer questions that iris biometrics cannot answer.     

基于Snake模型的虹膜定位算法

虹膜识别被认为是目前最准确可靠的生物特征识别方法．快速、准确地定位虹膜是虹膜识别系统的关键．提出一种基于Snake模型的虹膜定位算法：采用Canny检测算子定位虹膜内边缘，运用Snake模型锁定虹膜外边缘．实验表明，该方法速度快、精度高，并且具备良好的鲁棒性．     

Astute,fine and fast method of iris segmentation in unlimited circumstances

Neural Computing and Applications - Currently, Iris detection is considered as a significant module for robust biometric systems and high-speed applications such as eye tracking. Most iris...     

Biometric keys: suitable use cases and achievable information content

This article surveys use cases for cryptographic keys extracted from biometric templates (“biometric keys”). It lays out security considerations that favor uses for the protection of the confidentiality and privacy of biometric information itself. It is further argued that the cryptographic strength of a biometric key is determined by its true information content. I propose an idealized model of a biometric system as a Shannon channel. The information content that can be extracted from biometric templates in the presence of noise is determined within this model. The performance of state-of-the-art biometric technology to extract a key from a single biometric feature (like, e.g., one iris pattern or one fingerprint) is analyzed. Under reasonable operating conditions the channel capacity limits the maximal achievable information content k of biometric key to values smaller than about 30 bits. This upper length limit is too short to thwart “brute force” attacks on crypto systems employing biometric keys. The extraction of sufficiently long biometric keys requires either: (a) technological improvements that improve the recognition power of biometric systems considerably or (b) the employment of multimodal and/or multiinstance biometrics or (c) the use of novel biometric features, such as, e.g., the pattern DNA nucleotides in the human genome.     

Local intensity variation analysis for iris recognition

As an emerging biometric for human identification, iris recognition has received increasing attention in recent years. This paper makes an attempt to reflect shape information of the iris by analyzing local intensity variations of an iris image. In our framework, a set of one-dimensional (1D) intensity signals is constructed to contain the most important local variations of the original 2D iris image. Gaussian-Hermite moments of such intensity signals reflect to a large extent their various spatial modes and are used as distinguishing features. A resulting high-dimensional feature vector is mapped into a low-dimensional subspace using Fisher linear discriminant, and then the nearest center classifier based on cosine similarity measure is adopted for classification. Extensive experimental results show that the proposed method is effective and encouraging.     

A machine-vision system for iris recognition

This paper describes a prototype system for personnel verification based on automated iris recognition. The motivation for this endevour stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric measurement. In particular, it is known in the biomedical community that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body, its appearance is amenable to remote examination with the aid of a machine-vision system. The body of this paper details the design and operation of such a system. Also presented are the results of an empirical study in which the system exhibits flawless performance in the evaluation of 520 iris images.     

EyeCerts

In this paper, we propose EyeCerts, a biometric system for the identification of people which achieves offline verification of certified, cryptographically secure documents. An EyeCert is a printed document which certifies the association of content on the document with a biometric feature-a compressed version of a human iris in this work. The system is highly cost-effective since it does not require high complexity, hard-to-replicate printing technologies. Further, the device used to verify an EyeCert is inexpensive, estimated to have approximately the same cost as an off-the-shelf iris-scanning camera. As a central component of the EyeCert system, we present an iris analysis technique that aims to extract and compress the unique features of a given iris with a discrimination criterion using limited storage. The compressed features should be at maximal distance with respect to a reference iris image database. The iris analysis algorithm performs several steps in three main phases: 1) the algorithm detects the human iris by using a new model which is able to compensate for the noise introduced by the surrounding eyelashes and eyelids, 2) it converts the isolated iris using a modified Fourier-Mellin transform into a standard domain where the common radial patterns of the human iris are concisely represented, and 3) it optimally selects, aligns, and near-optimally compresses the most distinctive transform coefficients for each individual user. Using a low-quality imaging system (sub-U.S.$100), a /spl chi//sup 2/ error distribution model, and assuming a fixed false negatives rate of 5%, EyeCert caused false positives at rates better than 10/sup -5/ and as low as 10/sup -30/ for certain users.     

基于Gabor滤波的虹膜识别系统设计

随着科学技术的发展,信息安全在各个领域显得越来越重要,生物识别技术由于其特有的性质,在传统的识别领域中脱颖而出;其中,虹膜识别以高可靠性和差异性,成为目前安全性最高,最理想的生物识别技术;为增强虹膜识别算法的定位效率,提出一种改进定位算法,即二值化分割出瞳孔区域时,修正瞳孔边界,提高虹膜定位精度,有效提高识别速度;虹膜识别算法是整个系统的核心,要使虹膜识别具有良好的效果,需要对虹膜图像进行一系列的处理;为了完整高效地实现虹膜识别系统,对Gabor滤波器的原理及实现方法进行具体分析,研究Daugman虹膜识别算法,设计并完成了虹膜图像预处理,特征提取,二维Gabor滤波器的构建及参数选取等,经仿真实验,能够非常高效地完成虹膜特征提取并识别比对,计算速度和效果均优于传统算法。     

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.