Social Behavioral Information Fusion in Multimodal Biometrics
01 Dec 2018-IEEE Transactions on Systems, Man, and Cybernetics (IEEE)-Vol. 48, Iss: 12, pp 2176-2187
TL;DR: A novel person recognition approach is presented, that relies on the knowledge of individuals’ social behavior to enhance the performance of a traditional biometric system.
Abstract: The goal of a biometric recognition system is to make a human-like decisions on individual’s identity by recognizing their physiological and/or behavioral traits. Nevertheless, the decision-making process by either a human or a biometric recognition system can be highly complicated due to low quality of data or an uncertain environment. Human brain has an advantage over computer system due to its ability to perform a massive parallel processing of auxiliary information, such as visual cues, cognitive and social interactions, contextual, and spatio-temporal data. Similarly to a human brain, social behavioral cues can aid the reliable decision-making of an automated biometric system. In this paper, a novel person recognition approach is presented, that relies on the knowledge of individuals’ social behavior to enhance the performance of a traditional biometric system. The social behavioral information of individuals’ has been mined from an online social network and fused with traditional face and ear biometrics. Experimental results on individual’s and semi-real databases demonstrate significant performance gain in the proposed method over traditional biometric system.
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TL;DR: A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is presented in this paper, where the authors provide a comprehensive overview of the role of information fusion in biometrics.
151 citations
TL;DR: This paper proposes a hybrid ECG feature extraction method that integrated fiducial- and non-fiducials-based features to extract more comprehensive ECG features and thereby improve the authentication stability.
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TL;DR: The applications of Microsoft Kinect in cognitive systems for smart CE are introduced, and, using Kinect sensors, a human-behavior cognition technology is presented for gesture and activity recognition.
Abstract: Cognitive consumer electronics (CE), the fastest-growing sector worldwide that is driven by machine intelligence and cognitive systems, is triggered and enabled by audio- and video-capturing devices, smart sensors, health- and fitness-monitoring devices, security and education electronics, and intelligent systems. Smart consumer sensors and cognitive systems are synergized through the Internet of Things (IoT) for optimal information sharing, communication, real-time updates, data analytics, and enhanced support for decision making. Biometric-based devices, originally intended for large-scale applications in airports, border controls, disaster zones, or refugee migration zones, are enabling a wide range of applications in commercial and consumer sectors as standalone systems or with interconnected sensor networks. This article introduces the applications of Microsoft Kinect in cognitive systems for smart CE, and, using Kinect sensors, a human-behavior cognition technology is presented for gesture and activity recognition. As a novel front end of pervasive cognitive systems, the challenges and applications of a Kinect sensor-based system will be explored in CE, such as smart automobiles, health care, surveillance, and activity recognition.
51 citations
Additional excerpts
...This information is treated as social behavioral gait-based biometrics, and enhance user authentication [23], [24]....
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Posted Content•
TL;DR: The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics with specific focus on three questions: what to fusion, when to fuse, and how to fuse.
Abstract: The performance of a biometric system that relies on a single biometric modality (e.g., fingerprints only) is often stymied by various factors such as poor data quality or limited scalability. Multibiometric systems utilize the principle of fusion to combine information from multiple sources in order to improve recognition accuracy whilst addressing some of the limitations of single-biometric systems. The past two decades have witnessed the development of a large number of biometric fusion schemes. This paper presents an overview of biometric fusion with specific focus on three questions: what to fuse, when to fuse, and how to fuse. A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is also presented. In this regard, the following topics are discussed: (i) incorporating data quality in the biometric recognition pipeline; (ii) combining soft biometric attributes with primary biometric identifiers; (iii) utilizing contextual information to improve biometric recognition accuracy; and (iv) performing continuous authentication using ancillary information. In addition, the use of information fusion principles for presentation attack detection and multibiometric cryptosystems is also discussed. Finally, some of the research challenges in biometric fusion are enumerated. The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics.
47 citations
TL;DR: Various types and ways of authentication are surveyed, designed and developed primarily to secure the access to smartphones and attempts to clarify correlated buzzwords, with the motivation to assist new researchers in understanding the gist behind those concepts.
Abstract: Smartphones are the most popular and widespread personal devices. Apart from their conventional use, that is, calling and texting, they have also been used to perform multiple security sensitive activities, such as online banking and shopping, social networking, taking pictures, and e-mailing. On a positive side, smartphones have improved the quality of life by providing multiple services that users desire, for example, anytime-anywhere computing. However, on the other side, they also pose security and privacy threats to the users’ stored data. User authentication is the first line of defense to prevent unauthorized access to the smartphone. Several authentication schemes have been proposed over the years; however, their presentation might be perplexing to the new researchers to this domain, under the shade of several buzzwords, for example, active, continuous, implicit, static, and transparent, being introduced in academic papers without comprehensive description. Moreover, most of the reported authentication solutions were evaluated mainly in terms of accuracy, overlooking a very important aspect—the usability. This paper surveys various types and ways of authentication, designed and developed primarily to secure the access to smartphones and attempts to clarify correlated buzzwords, with the motivation to assist new researchers in understanding the gist behind those concepts. We also present the assessment of existing user authentication schemes exhibiting their security and usability issues.
38 citations
Cites background from "Social Behavioral Information Fusio..."
...Touch [9, 113]; keystroke [115]; hold [8]; gait [116–118]; behavior profiling [119] Adaptive; continuous; multimodal; risk-based; transparent [3, 5, 10, 113, 119–121] [8, 12, 29, 112, 113, 115–117, 122–127] 8 Mobile Information Systems...
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...[119] combined social behavioral information of individuals that was extracted from the online social networks to fuse with traditional face and ear biometrics, to enhance the performance of the traditional biometric systems....
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References
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TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Abstract: We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed "Fisherface" method has error rates that are lower than those of the eigenface technique for tests on the Harvard and Yale face databases.
11,674 citations
"Social Behavioral Information Fusio..." refers methods in this paper
...The feature extraction process using PCA is described below [26]....
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03 Jun 1991
TL;DR: An approach to the detection and identification of human faces is presented, and a working, near-real-time face recognition system which tracks a subject's head and then recognizes the person by comparing characteristics of the face to those of known individuals is described.
Abstract: An approach to the detection and identification of human faces is presented, and a working, near-real-time face recognition system which tracks a subject's head and then recognizes the person by comparing characteristics of the face to those of known individuals is described. This approach treats face recognition as a two-dimensional recognition problem, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. Face images are projected onto a feature space ('face space') that best encodes the variation among known face images. The face space is defined by the 'eigenfaces', which are the eigenvectors of the set of faces; they do not necessarily correspond to isolated features such as eyes, ears, and noses. The framework provides the ability to learn to recognize new faces in an unsupervised manner. >
5,489 citations
Additional excerpts
...The feature extraction process using PCA is described below [26]....
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...PCA [25], a bench-mark appearance-based method, has been used to extract face and ear features....
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...In 2003, Chang et al. [13] proposed a multimodal system using face and ear biometrics, where features are extracted using principle component analysis (PCA) and fused at feature level....
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...PCA [25], a bench-mark appearance-based method,...
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TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.
Abstract: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance.
4,816 citations
"Social Behavioral Information Fusio..." refers background or methods in this paper
...For each train and test set, two face samples of 241 subjects are randomly picked from FERET, AR, AT&T, VidTIMIT, and UOM face DBs. Similarly, two sets of 241 ear samples are randomly picked from USTB-I, IIT Delhi, and UOM ear DB as train and test sets....
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...FERET [30] DB consists of 14 051 eight-bit gray-scale images of 1199 individuals....
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...We used face and ear samples from six publicly available standard face and ear DBs: 1) FERET [30]; 2) AR [31]; 3) AT&T [40]; 4) VidTIMIT [41]; 5) USTB I [32]; and 6) IIT Delhi [42] and a recently created real multimodal DB: UOM multimodal face and ear DB [43]....
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TL;DR: A brief overview of the field of biometrics is given and some of its advantages, disadvantages, strengths, limitations, and related privacy concerns are summarized.
Abstract: A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.
4,678 citations
"Social Behavioral Information Fusio..." refers background in this paper
...is harder to forge, steal, transfer, or forget biometric data [1], [21]....
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...It determines a person’s identity based on physiological or behavioral biometric traits [1]....
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TL;DR: In this article, the authors show that when the training data set is small, PCA can outperform LDA and, also, that PCA is less sensitive to different training data sets.
Abstract: In the context of the appearance-based paradigm for object recognition, it is generally believed that algorithms based on LDA (linear discriminant analysis) are superior to those based on PCA (principal components analysis). In this communication, we show that this is not always the case. We present our case first by using intuitively plausible arguments and, then, by showing actual results on a face database. Our overall conclusion is that when the training data set is small, PCA can outperform LDA and, also, that PCA is less sensitive to different training data sets.
3,102 citations