Revocable and non-invertible multibiometric template protection based on matrix transformation

Biometric authentication refers to the use of measurable characteristics (or features) of the human body to provide secure, reliable and convenient access to a computer system or physical environment. These features (physiological or behavioural) are unique to individual subjects because they are usually obtained directly from their owner's body. Multibiometric authentication systems use a combination of two or more biometric modalities to provide improved performance accuracy without offering adequate protection against security and privacy attacks. This paper proposes a multibiometric matrix transformation based technique, which protects users of multibiometric systems from security and privacy attacks. The results of security and privacy analyses show that the approach provides high-level template security and user privacy compared to previous one-way transformation techniques

Iris Recognition Approach for Preserving Privacy in Cloud Computing

Biometric identification systems involve securing biometric traits by encrypting them using an encryption algorithm and storing them in the cloud. In recent decades, iris recognition schemes have been considered one of the most effective biometric models for identifying humans based on iris texture, due to their relevance and distinctiveness. The proposed system focuses on encrypting biometric traits. The user’s iris feature vector is encrypted and stored in the cloud. During the matching process, the user’s iris feature vector is compared with the one stored in the cloud. If it meets the threshold conditions, the user is authenticated. Iris identification in cloud computing involves several steps. First, the iris image is pre-processed to remove noise using the Hough transform. Then, the pixel values are normalized, Gabor filters are applied to extract iris features. The features are then encrypted using the AES 128-bit algorithm. Finally, the features of the test image are matched with the stored features on the cloud to verify authenticity. The process ensures the privacy and security of the iris data in cloud storage by utilizing encryption and efficient image processing techniques. The matching is performed by setting an appropriate threshold for comparison. Overall, the approach offers a significant level of safety, effectiveness, and accuracy

Multibiometric security in wireless communication systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims

Simple and secured access to networked home appliances via internet using SSL, BioHashing and single Authentication Server

This thesis describes a web-based application that will enable users to access their networked home appliances over the Internet in an easy, secured, accessible and cost effective manner, using the user's iris image only for authentication. As Internet is increasingly gaining significance and popularity in our daily lives, various home networking technologies also started gaining importance from consumers, which helped in facilitating interoperability, sharing of services and exchange of information between different electronic devices at home. As a result, the demand to be able to access home appliances or security cameras over the Internet gradually grew. In this research, we propose an efficient, secured, low-cost and user-friendly method to access networked home appliances over the Internet, providing strong, well integrated, three levels of security to the whole application and user data. According to our design, the user's iris data after hashing (using BioHashing) is sent through a secure communication channel utilizing Secure Sockets Layer v-3.0. The deterministic feature sequence from the iris image is extracted using 1D log-Gabor filters and while performing BioHashing, the orthonormalization of the pseudorandom number is implemented employing Gram-Schmidt orthonormalization algorithm. In addition to this protected data transfer mechanism, we propose the design of an Authentication Server that can be shared among multiple homes, allowing numerous users to access their home appliances in a trouble-free and secured manner. It can also bring down the cost of commercial realization of this endeavor and increase its accessibility without compromising on system security. We demonstrate that the recognition efficiency of this system is computationally effective with equal error rate (EER) of 0% and 6.75% (average) in two separate conditions on CASIA 1 and CASIA 2 iris image datasets

Multibiometric security in wireless communication systems

This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Security/privacy analysis of biometric hashing and template protection for fingerprint minutiae

This thesis has two main parts. The first part deals with security and privacy analysis of biometric hashing. The second part introduces a method for fixed-length feature vector extraction and hash generation from fingerprint minutiae. The upsurge of interest in biometric systems has led to development of biometric template protection methods in order to overcome security and privacy problems. Biometric hashing produces a secure binary template by combining a personal secret key and the biometric of a person, which leads to a two factor authentication method. This dissertation analyzes biometric hashing both from a theoretical point of view and in regards to its practical application. For theoretical evaluation of biohashes, a systematic approach which uses estimated entropy based on degree of freedom of a binomial distribution is outlined. In addition, novel practical security and privacy attacks against face image hashing are presented to quantify additional protection provided by biometrics in cases where the secret key is compromised (i.e., the attacker is assumed to know the user's secret key). Two of these attacks are based on sparse signal recovery techniques using one-bit compressed sensing in addition to two other minimum-norm solution based attacks. A rainbow attack based on a large database of faces is also introduced. The results show that biometric templates would be in serious danger of being exposed when the secret key is known by an attacker, and the system would be under a serious threat as well. Due to its distinctiveness and performance, fingerprint is preferred among various biometric modalities in many settings. Most fingerprint recognition systems use minutiae information, which is an unordered collection of minutiae locations and orientations Some advanced template protection algorithms (such as fuzzy commitment and other modern cryptographic alternatives) require a fixed-length binary template. However, such a template protection method is not directly applicable to fingerprint minutiae representation which by its nature is of variable size. This dissertation introduces a novel and empirically validated framework that represents a minutiae set with a rotation invariant fixed-length vector and hence enables using biometric template protection methods for fingerprint recognition without signi cant loss in verification performance. The introduced framework is based on using local representations around each minutia as observations modeled by a Gaussian mixture model called a universal background model (UBM). For each fingerprint, we extract a fixed length super-vector of rst order statistics through alignment with the UBM. These super-vectors are then used for learning linear support vector machine (SVM) models per person for verifiation. In addition, the xed-length vector and the linear SVM model are both converted into binary hashes and the matching process is reduced to calculating the Hamming distance between them so that modern cryptographic alternatives based on homomorphic encryption can be applied for minutiae template protection

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers

Sparse Methods for Robust and Efficient Visual Recognition

Visual recognition has been a subject of extensive research in computer vision. A vast literature exists on feature extraction and learning methods for recognition. However, due to large variations in visual data, robust visual recognition is still an open problem. In recent years, sparse representation-based methods have become popular for visual recognition. By learning a compact dictionary of data and exploiting the notion of sparsity, start-of-the-art results have been obtained on many recognition tasks. However, existing data-driven sparse model techniques may not be optimal for some challenging recognition problems. In this dissertation, we consider some of these recognition tasks and present approaches based on sparse coding for robust and efficient recognition in such cases. First we study the problem of low-resolution face recognition. This is a challenging problem, and methods have been proposed using super-resolution and machine learning based techniques. However, these methods cannot handle variations like illumination changes which can happen at low resolutions, and degrade the performance. We propose a generative approach for classifying low resolution faces, by exploiting 3D face models. Further, we propose a joint sparse coding framework for robust classification at low resolutions. The effectiveness of the method is demonstrated on different face datasets. In the second part, we study a robust feature-level fusion method for multimodal biometric recognition. Although score-level and decision-level fusion methods exist in biometric literature, feature-level fusion is challenging due to different output formats of biometric modalities. In this work, we propose a novel sparse representation-based method for multimodal fusion, and present experimental results for a large multimodal dataset. Robustness to noise and occlusion are demonstrated. In the third part, we consider the problem of domain adaptation, where we want to learn effective classifiers for cases where the test images come from a different distribution than the training data. Typically, due to high cost of human annotation, very few labeled samples are available for images in the test domain. Specifically, we study the problem of adapting sparse dictionary-based classification methods for such cases. We describe a technique which jointly learns projections of data in the two domains, and a latent dictionary which can succinctly represent both domains in the projected low dimensional space. The proposed method is efficient and performs on par or better than many competing state-of-the-art methods. Lastly, we study an emerging analysis framework of sparse coding for image classification. We show that the analysis sparse coding can give similar performance as the typical synthesis sparse coding methods, while being much faster at sparse encoding. In the end, we conclude the dissertation with discussions and possible future directions