Towards a multi-source fusion approach for eye movement-driven recognition

This paper presents a research for the use of multi-source information fusion in the field of eye movement biometrics. In the current state-of-the-art, there are different techniques developed to extract the physical and the behavioral biometric characteristics of the eye movements. In this work, we explore the effects from the multi-source fusion of the heterogeneous information extracted by different biometric algorithms under the presence of diverse visual stimuli. We propose a two-stage fusion approach with the employment of stimulus-specific and algorithm-specific weights for fusing the information from different matchers based on their identification efficacy. The experimental evaluation performed on a large database of 320 subjects reveals a considerable improvement in biometric recognition accuracy, with minimal equal error rate (EER) of 5.8%, and best case Rank-1 identification rate (Rank-1 IR) of 88.6%. It should be also emphasized that although the concept of multi-stimulus fusion is currently evaluated specifically for the eye movement biometrics, it can be adopted by other biometric modalities too, in cases when an exogenous stimulus affects the extraction of the biometric features.     

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.     

A novel image hiding approach based on correlation analysis for secure multimodal biometrics

This paper proposes a novel multimodal biometric images hiding approach based on correlation analysis, which is used to protect the security and integrity of transmitted multimodal biometric images for network-based identification. Compared with existing methods, the correlation between the biometric images and the cover image is first analyzed by partial least squares (PLS) and particle swarm optimization (PSO), aiming to make use of the abundant information of cover image to represent the biometric images. Representing the biometric images using the corresponding content of cover image results in the generation of the residual images with much less energy. Then, considering the human visual system (HVS) model, the residual images as the secret images are embedded into the cover image using middle-significant-bit (MSB) method. Extensive experimental results demonstrate that the proposed approach not only provides good imperceptibility but also resists some common attacks and assures the effectiveness of network-based multimodal biometrics identification.     

Multimodal biometrics: Empirical study of performance-throughput trade-off

The paper briefly describes results of empirical study on performance (as measured by ROC) and throughput (as measured by number of matches per sec) of multimodal biometrics. We use cascaded multimodal biometric identification. Experiments show that cascaded multimodal biometric fusion improves both throughput and performance.     

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.     

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 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.     

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).     

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.

     

Score level fusion of multimodal biometrics using triangular norms

A multimodal biometric system that alleviates the limitations of the unimodal biometric systems by fusing the information from the respective biometric sources is developed. A general approach is proposed for the fusion at score level by combining the scores from multiple biometrics using triangular norms (t-norms) due to Hamacher, Yager, Frank, Schweizer and Sklar, and Einstein product. This study aims at tapping the potential of t-norms for multimodal biometrics. The proposed approach renders very good performance as it is quite computationally fast and outperforms the score level fusion using the combination approach (min, mean, and sum) and classification approaches like SVM, logistic linear regression, MLP, etc. The experimental evaluation on three databases confirms the effectiveness of score level fusion using t-norms.     

Presentation attack detection based on score level fusion and challenge-response technique

Biometrics is the state of the art in dealing with identity identification and verification based on the physical and behavioral characteristics and widely used in the fields of Fintech, such as mobile payment and online banking due to its security and convenience. However, there are various attacks against the biometrics system. The presentation attack is one of the most common attacks that an imposter presents fake biometrics to the sensor trying to fool the system. This paper proposes a multimodal presentation attack detection (PAD) method against photo-attack and video-attack in face recognition system by using score level fusion and challenge-response scenario. The proposed challenge-response scenario is that requesting the user to speak out the randomly prompted words. Then, the recognized speech text and the user’s mouth motion are detected simultaneously to verify if the user is liveness. Two weighted score level fusion rules, namely weighted sum and weight product, are used to combine the speech and mouth motion traits as a matching score. The final score is fed into supervised machine learning algorithms and trained for classifying spoofing. The experiments are conducted in the self-built database. Experimental results show that the proposed method can achieve the best half total error rate at 3.64% and can effectively improve facial recognition system security.

     

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.     

Physiological and behavioral lip biometrics: A comprehensive study of their discriminative power

Compared with other traditional biometric features such as face, fingerprint, or handwriting, lip biometric features contain both physiological and behavioral information. Physiologically, different people have different lips. On the other hand, people can usually be differentiated by their talking style. Current research on lip biometrics generally does not distinguish between the two kinds of information during feature extraction and classification and the interesting question of whether the physiological or the behavioral lip features are more discriminative has not been comprehensively studied. In this paper, different physiological and behavioral lip features are studied with respect to their discriminative power in speaker identification and verification. Our experimental results have shown that both the static lip texture feature and the dynamic shape deformation feature can achieve high identification accuracy (above 90%) and low verification error rate (below 5%). In addition, the lip rotation and centroid deformations, which are related to the speaker's talking mannerism, are found to be useful for speaker identification and verification. In contrast to previous studies, our results show that behavioral lip features are more discriminative in speaker identification and verification compared to physiological features.     

基于匹配分数的多生物特征识别融合综述

受数据噪音和识别系统本身的限制,基于单一生物特征的身份认证系统所能达到的准确率是有限的。通过多生物特征识别来提高识别的准确率成为当前生物特征识别领域的研究热点之一。首先介绍了多生物特征识别的必要性、多生物特征识别的分类,然后重点介绍了基于匹配分数的多生物特征识别融合的研究现状,最后总结了多生物特征识别研究中的问题和未来的研究方向。     

Implementation of Biometric Systems — Security and Privacy Considerations

As biometric systems are deployed within security systems, or as part of identification programs, implementation issues relating to security and privacy need to be considered. The role of a biometric system is to recognize (or not) an individual through specific physiological or behavioral traits. The use of the word ‘recognize’ is significant — defined in the Oxford Dictionary as “identify as already known”. In other words, a biometric system does not establish the identity of an individual in any way, it merely recognizes that they are who they say they are (in a verification or a ‘positive identification’ system), or that they were not previously known to the system (in a ‘negative identification’ system, for example, to avoid double enrollment in a welfare system). This tie between the actual identity of an individual and the use of biometrics is subtle and provokes much debate, particularly relating to privacy and other societal issues. This paper seeks to clarify come of these issues by providing a framework, and by distinguishing between technology and societal issues.     

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.     

Biometric template protection with DCT-based watermarking

In this paper we have addressed a solution of two big issues in design of multimodal system: template protection and fusion strategy. A robust biometric watermarking algorithm is proposed for biometric template protection. The fingerprint feature vector and iris features are used as watermark. Proposed DCT-based watermarking technique embeds watermark in low-frequency AC coefficients of selected 8 $\times $ 8 DCT smoother blocks. Blocks are classified based on human visual system. The robustness of the proposed algorithm is compared with the few state-of-art literature when watermarked image is subjected to possible channel attacks. Decision level fusion strategy is used to improve the overall performance of multimodal system. That is achieved by conditionally limiting the threshold of the fingerprint system to a maximum value, obtained by projecting 50 % of the cross over error rate on to the FRR curve of the iris system.     

Biometrics in Identity Management Systems

Biometric technology - the automated recognition of individuals using biological and behavioral traits - has been presented as a natural identity management tool that offers "greater security and convenience than traditional methods of personal recognition." Indeed, many existing government identity management systems employ biometrics to assure that each person has only one identity in the system and that only one person can access each identity. Historically, however, biometric technology has also been controversial, with many writers suggesting that biometrics invade privacy, that specific technologies have error rates unsuitable for large-scale applications, or that the techniques "are useful to organizations that regulate the individual, but of little use where the individual controls identification and authorization." Here, I address these controversies by looking more deeply into the basic assumptions made in biometric recognition. I'll look at some example systems and delve into the differences between personal identity and digital identity. I'll conclude by discussing how those whose identity is managed with biometrics can manage biometric identity management.     

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.