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.     

Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

Due to the enormous usage of the internet for transmission of data over a network, security and authenticity become major risks. Major challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database server. To describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical (Face, fingerprint, Ear etc.) and behavioural (Gesture, Voice, tying etc.) by means of matching and verification process. In this work, biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential data. The proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary templates. Here Face Attribute Convolutional Neural Network (FACNN) is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further processing. Implementing Artificial Intelligence (AI) into the proposed system, automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while processing. Binary codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and verification. Three main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify authenticity. This system enhances the system performance by 8% matching and verification and minimizes the False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER) by 6 times and increases the data privacy through the biometric cryptosystem by 98.2% while compared to other work.     

Enhancing performance and user convenience of multi-biometric verification systems

Pattern Analysis and Applications - A multi-biometric verification system lowers the verification errors by fusing information from multiple biometric sources. Information can be fused in parallel...     

Usability evaluation of multi-modal biometric verification systems

As a result of the evolution in the field of biometrics, a new breed of techniques and methods for user identity recognition and verification has appeared based on the recognition and verification of several biometric features considered unique to each individual. Signature and voice characteristics, facial features, and iris and fingerprint patterns have all been used to identify a person or just to verify that the person is who he/she claims to be. Although still relatively new, these new technologies have already reached a level of development that allows its commercialization. However, there is a lack of studies devoted to the evaluation of these technologies from a user-centered perspective. This paper is intended to promote user-centered design and evaluation of biometric technologies. Towards this end, we have developed a platform to perform empirical evaluations of commercial biometric identity verification systems, including fingerprint, voice and signature verification. In this article, we present an initial empirical study in which we evaluate, compare and try to get insights into the factors that are crucial for the usability of these systems.     

A novel biometric system for signature verification based on score level fusion approach

Multimedia Tools and Applications - The active modality of handwriting is broadly related to signature verification in the context of biometric user authentication systems. Signature verification...     

基于虹膜生物特征信息的图像加密方法

针对目前图像加密中的密钥安全性问题,提出一种基于虹膜生物特征信息的图像加密方法。该方法一方面确保了密匙的安全性;另一方面,密匙的获取无须传送。它通过虹膜采集系统的在线认证,使得具有授权的用户方能正确获取密钥,实现图像解密。与此同时,着重强调当用同一生物特征信息对多幅不同图像进行加密时,采用独立变量分析法能有效分离出密钥信息或原图像。     

Based blockchain-PSO-AES techniques in finger vein biometrics: A novel verification secure framework for patient authentication

The main objective of this study is to propose a novel verification secure framework for patient authentication between an access point (patient enrolment device) and a node database. For this purpose, two stages are used. Firstly, we propose a new hybrid biometric pattern model based on a merge algorithm to combine radio frequency identification and finger vein (FV) biometric features to increase the randomisation and security levels in pattern structure. Secondly, we developed a combination of encryption, blockchain and steganography techniques for the hybrid pattern model. When sending the pattern from an enrolment device (access point) to the node database, this process ensures that the FV biometric verification system remains secure during authentication by meeting the information security standard requirements of confidentiality, integrity and availability. Blockchain is used to achieve data integrity and availability. Particle swarm optimisation steganography and advanced encryption standard techniques are used for confidentiality in a transmission channel. Then, we discussed how the proposed framework can be implemented on a decentralised network architecture, including access point and various databases node without a central point. The proposed framework was evaluated by 106 samples chosen from a dataset that comprises 6000 samples of FV images. Results showed that (1) high-resistance verification framework is protected against spoofing and brute-force attacks; most biometric verification systems are vulnerable to such attacks. (2) The proposed framework had an advantage over the benchmark with a percentage of 55.56% in securing biometric templates during data transmission between the enrolment device and the node database.     

On the error-reject trade-off in biometric verification systems

We address the problem of performance evaluation in biometric verification systems. By formulating the optimum Bayesian decision criterion for a verification system and by assuming the data distributions to be multinormals, we derive two statistical expressions for calculating theoretically the false acceptance and false rejection rates. Generally, the adoption of a Bayesian parametric model does not allow for obtaining explicit expressions for the calculation of the system errors. As far as biometric verification systems are concerned, some hypotheses can be reasonably adopted, thus allowing simple and affordable expressions to be derived. By using two verification system prototypes. Based on hand shape and human face, respectively, we show our results are well founded     

构建基于Intel PXA255的指纹识别系统

指纹识别是身份验证中一种重要的生物识别技术.指纹具有唯一性、不变性、易于获取等特性,这使得指纹识别成为生物鉴定学中最为成熟的方式.嵌入式指纹识别系统集成了模式识别,图像处理,智能卡,数据库等技术,适用于多种应用场合,应用前景广阔.下面讨论基于Intel XScale PXA255处理器和Windows CE.net嵌入式操作系统构建自动指纹识别系统.     

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.     

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.     

Improving the accuracy and storage cost in biometric remote authentication schemes

Recently, Bringer et al. proposed a new approach for remote biometric based verification, which consists of a hybrid protocol that distributes the server side functionality in order to detach the biometric data storage from the service provider. Besides, a new security model is defined using the notions of Identity and Transaction Privacy, which guarantee the privacy of the identity-biometrics relationship under the assumption of non-colluding servers. However, due to the high communication and computational costs, the systems following this model cannot be implemented for large scale biometric systems.In this paper, we describe an efficient multi-factor biometric verification system with improved accuracy and lower complexity by considering the range information of every component of the user biometrics separately. Also, the new scheme is provably secure based on the security model of Bringer et al and implements a different database storage that eliminates the disadvantages of encrypted biometric templates in terms of ciphertext expansion. Also, we evaluate different Private Information Retrieval (PIR) schemes applicable for this setting and propose a practical solution for our scheme that reduces the computation costs dramatically. Finally, we compare our results with existing provably secure schemes and achieve reduced computational cost and database storage cost due to the single storage of the common features of the users in the system and amortization of the time complexity of the PIR.     

基于演化计算的在线手写签名验证

在线手写签名验证是目前生物特征识别领域较为热门的一个方向,论文试图将演化计算的思想,用于在线签名验证。在建立数学模型的基础上,提出了签名验证匹配演化算法,并给出了计算实例。通过对匹配结果的分析,证明了该方法的可行性。     

A multimodal-Siamese Neural Network (mSNN) for person verification using signatures and EEG

Signatures have long been considered to be one of the most accepted and practical means of user verification, despite being vulnerable to skilled forgers. In contrast, EEG signals have more recently been shown to be more difficult to replicate, and to provide better biometric information in response to known a stimulus. In this paper, we propose combining these two biometric traits using a multimodal Siamese Neural Network (mSNN) for improved user verification. The proposed mSNN network learns discriminative temporal and spatial features from the EEG signals using an EEG encoder and from the offline signatures using an image encoder. Features of the two encoders are fused into a common feature space for further processing. A Siamese network then employs a distance metric based on the similarity and dissimilarity of the input features to produce the verification results. The proposed model is evaluated on a dataset of 70 users, comprised of 1400 unique samples. The novel mSNN model achieves a 98.57% classification accuracy with a 99.29% True Positive Rate (TPR) and False Acceptance Rate (FAR) of 2.14%, outperforming the current state-of-the-art by 12.86% (in absolute terms). This proposed network architecture may also be applicable to the fusion of other neurological data sources to build robust biometric verification or diagnostic systems with limited data size.     

Finger surface as a biometric identifier

We present a novel approach for personal identification and identity verification which utilizes 3D finger surface features as a biometric identifier. Using 3D range images of the hand, a surface representation for the index, middle, and ring finger is calculated and used for comparison to determine subject similarity. We use the curvature based shape index to represent the fingers’ surface. Gallery and probe shape index signatures are compared using the normalized correlation coefficient to compute a match score. A large unique database of hand images supports the research. We use data sets obtained over time to examine the performance of each individual finger surface as a biometric identifier as well as the performance obtained when combining them. Both identification and verification experiments are conducted. In addition, probe and gallery sets sizes are increased to further improve recognition performance in our experiments. Our approach yields good results for a first-of-its-kind biometric technique, indicating that this approach warrants further research.     

Group-specific face verification using soft biometrics

Soft biometrics have been recently proposed for improving the verification performance of biometric recognition systems. Examples of soft biometrics are skin, eyes, hair colour, height, and ethnicity. Some of them are often cheaper than “hard”, standard biometrics (e.g., face and fingerprints) to extract. They exhibit a low discriminant power for recognizing persons, but can add some evidences about the personal identity, and can be useful for a particular set of users. In particular, it is possible to argue that users with a certain high discriminant soft biometric can be better recognized. Identifying such users could be useful in exploiting soft biometrics at the best, as deriving an appropriate methodology for embedding soft-biometric information into the score computed by the main biometric.In this paper, we propose a group-specific algorithm to exploit soft-biometric information in a biometric verification system. Our proposal is exemplified using hair colour and ethnicity as soft biometrics and face as biometric. Hair colour and information about ethnicity can be easily extracted from face images, and used only for a small number of users with highly discriminant hair colour or ethnicity. We show by experiments that for those users, hair colour or ethnicity strongly contributes to reduce the false rejection rate without a significant impact on the false acceptance rate, whilst the performance does not change for other users.     

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.

     

On error reduction by the symmetric rejection method in multi-stage biometric verification systems

Pattern Analysis and Applications - A multi-stage biometric verification system serially activates its verifiers and improves performance-cost trade-off by allowing users to submit a subset of the...     

Likelihood ratio based features for a trained biometric score fusion

In this work, we present a novel trained method for combining biometric matchers at the score level. The new method is based on a combination of machine learning classifiers trained using the match scores from different biometric approaches as features. The parameters of a finite Gaussian mixture model are used for modelling the genuine and impostor score densities during the fusion step.Several tests on different biometric verification systems (related to fingerprints, palms, fingers, hand geometry and faces) show that the new method outperforms other trained and non-trained approaches for combining biometric matchers.We have tested some different classifiers, support vector machines, AdaBoost of neural networks, and their random subspace versions, demonstrating that the choice for the proposed method is the Random Subspace of AdaBoost.     

PUG-FB : Person-verification using geometric and Haralick features of footprint biometric

Multimedia Tools and Applications - This article demonstrates a study of biometric identification and verification system using foot geometry features. A footprint has three types of features which...