This remains true even in the presence of erroneous, incomplete or missing data. The advantages of a multi-modal system over a traditional biometric system stem from the ability to analyze multiple sources of information. This means increased accuracy, fewer enrolment problems and enhanced security. All multi-modal biometric systems need a fusion module that takes individual data and combines it in order to obtain the authentication result: impostor or genuine user.
The decision making process in a fusion module may be as simple as performing a logical operation on single bits or as complex as an intelligent system developed using principles of fuzzy logic and cognitive informatics [14,19]. Throughout the last decade, a large number of multi-modal systems have been developed in a quest to find the optimum combination of biometric characteristics and fusion approaches to minimize recognition errors. The drive to develop new methodologies based on a fusion of features, algorithms and decision-making strategies can, on its own, be considered an intelligent approach to biometrics.
Information fusion originates with decision-making strategies in a multi-sensor system that relies on a number of signals coming from varied sensors or devices; it has been quickly adopted to the biometric security domain of research. The intrinsic characteristics of biometric data and processing resulted in a variety of algorithms for biometric information fusion evolving.
Two-factor authentication – News, Research and Analysis – The Conversation – page 1
Combine this with standard methods relying on feature, sensor, match-level, rank-level and decision-level of information fusion and new hybrid approaches emerge. Thus, a combination of machine learning and information fusion in biometric systems has been explored recently. These systems rely on a unique combination of neural networks , cognitive systems [16,19], fuzzy logic  and adaptive learning . The benefits of this approach are seen in increased performance, higher flexibility in matching biometric traits at different levels of decision-making, better circumvention resistance to errors , fewer opportunities for intruders to undermine system security, and most importantly, the system has the ability to dynamically adapt to new information, which could include new photographs, video images, soft biometrics height, weight, age, hair color etc , or a completely new trait such as infrared or vein biometrics.
The methodology can be further extended to relevant domains, including multimedia, text and image categorization, multi-modal image retrieval and web-based social network analysis.
Three new approaches have been recently introduced to biometric technology with implications for Cyberworld security:. These new approaches will provide a powerful and unique methodology for enhancing user-security in on-line communities, and society as a whole. Knowing this, a novel content-based image retrieval approach based on the weighted features of colour and shape will be used as the backbone of the Cyberworlds multi-modal recognition system. The well-known, simple, and fast feature extraction algorithms: colour histogram and Haar Discrete Wavelet Transform DWT can be utilized to extract appearance features.
Colour histogram method extracts the color features, and segmented matrix algorithm for Haar DWT can be applied to extract the shape attribute of an image. The weighted colour, texture, and shape features will then be combined in a single descriptor to reduce feature dimensionality and achieve reduction in required storage. To further adopt the proposed image retrieval system for on-line user authentication, texture feature can be included to boost the recognition accuracy. Next, the fusion of the three content-based low level features for appearance recognition in application to on-line user authentication takes place.
University of Louisville, USA, has collected a large avatar database through both manual and automated generation. The database has provided the basic test cases of empirical studies for Artimetrics, or the study of biometrics application to Artificial entities [20,21]. The reported results show that appearance-based methods, combined with multi-model fusion approach can be successfully used for validation of the developed methodology on avatar databases. The second direction will leverage the behavioural biometrics for abnormal behaviour detection and risk management in online community users.
In M. Lyons et. A recently published paper demonstrated the possibility of using behavioural biometic strategies designed to recognize humans to identify artificially created intelligent software agents used to gain an unfair advantage by some members of multiplayer online communities . The paper lays the theoretical groundwork for research in authentication of non-biological entities. The artificial intelligence learning methods based on chaotic neural networks can be successfully utilized to learn normal and abnormal user behavioural patterns .
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The third direction is focused on protecting user confidentiality in the Cyberworld. Privacy, for an online user, is of paramount importance. While traditional identification and verification methods ID, smart card, password are commonly used for on-line user authentication, biometrics are frequently more convenient for users, and come with the added benefit of reducing fraud and being more secure. In a traditional system, if a password is compromised the user can usually easily change it.
However, biometrics are unique to each user, and considered to be irrevocable until very recently. It has also been discovered that the accuracy performance can be increased by combining different feature data types and by increasing the input string length. The novel contributions of this paper are twofold.
FRAMEWORK FOR IMAGE BASED AUTHENTICATION
Firstly, it describes how a comprehensive experiment is set up to collect touch dynamics biometrics data, and the set of collected data is being made publically available, which may facilitate further research in the problem domain. Secondly, the paper demonstrates how the data set may be used to strengthen the protection of resources that are accessible via mobile devices. This research work was supported by the University of Manchester.
The authors would also like to thank all the people who have contributed their time and effort to the creation of this data set, and also to the reviewers for their valuable time and insightful comments. Teh, P. Emerald Group Publishing Limited. Please share your general feedback. Furthermore, this system will be implemented to enhance security for accessing control login government system. A review of fingerprint pre-processing using a mobile phone. The presented scheme utilizes discrete wavelet transform DWT and can locate the pixels of malicious The presented scheme utilizes discrete wavelet transform DWT and can locate the pixels of malicious tampering if present.
The wavelet coefficients are considered for embedding authentication code that are encrypted with a secret key, ensuring high security. The authentication binary code is embedded in a block of wavelet coefficients. The experimental results show that the proposed method can retain the image quality after watermarking process with minimum watermark payload and can achieve promising tampering detection.
The watermark is secure against local attacks because it is encrypted using secret key. We have also performed experiments to exhibit the fragility of the watermark against known attacks. Determination of the botanical origin of raw spirit used for alcoholic beverage production is of great importance for rectifying units, control laboratories, and proper product labeling.
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Raw spirit samples produced from rye, corn, Raw spirit samples produced from rye, corn, and potato were analyzed using a solid phase microextraction-mass spectrometry SPME-MS method, which involved volatiles preconcentration by SPME with subsequent volatile fraction characterization by MS without particular compounds separation by GC. Obtained data were treated using principal component analysis and linear discriminant analysis LDA to test the possibility of sample classification.
Use of LDA made possible the classification of raw spirits based on the material they were produced from. A Real Dynamic Cyber Trust Model is an application that is proposed in order to distinguish the trust belief among the trustees who have been marketing in today's digital world by authenticated users. Though we are happy with the Though we are happy with the developing technology, still we are worrying about the security issues in every scenario. In the same way, if we shop online by trusting some products, there are few chances of getting bad products. We can experience these types of scenarios when we shop online in some interfaces like amazon, e bay, flip kart, etc.
Of course, there are many existing systems which give a rating to the product, that help the buyer to trust the seller and buy the product. Still, there is an issue of being cheated by some wrong reviews given by unauthenticated users. A Real Dynamic Cyber Trust Model has taken the scenario of seller and buyer who goes shopping for products online. Both Modules of Seller and Buyer have the opportunity of getting registered and log in to the application. Buyer can give the feedback of any product he buys and checks the trust factor of any seller.
Whereas the Seller can add, update or delete the products he sells. Based on the feedback given by the buyer the Real Dynamic Cyber Trust Model calculates the trust factor of the seller, which helps the buyer to find out whether the seller is trustworthy or not. Kuchillapati Chinnari Dr. This paper explains the liveness detection of the biometric system using the Face-Iris Images and deep learning classification.
We have proposed novel hybrid algorithms for Face-Iris Liveness Recognition which can be used in cyber We have proposed novel hybrid algorithms for Face-Iris Liveness Recognition which can be used in cyber security authentication. In proposed model the individual identification is obtained from the extracted features that captured from face-Iris Images.
Iris recognition is one of the most important biometric traits in which the iris image will be processed by some type of reliable, robust, and fast algorithm to capture the unique features embedded in iris. The camera system in which is used to capture the iris image will register depicts changes and variations in size of Iris as exposed to the light during the checkup and individual identification.
This information will be used to verify the liveness of the iris image and distinguish the real lived iris image from the faked iris image. The recognition system based on the iris images are costly and required high resolution optical sensors and camera system beside the presence of the individual at the time of authentication and verification. The face image may change in certain degree such as appearance but shape and structure of face skeleton , location of eyes, mouth, and nose are remain unchanged.
Due to this remarkable characteristic and ability to generate the face image features with near perfect identifiers and lack of requirement for physical contact with recognition system has made the facial recognition system remarkable. Our proposed face-iris feature extraction has been used to detect the fake face or iris from the real one and genuine live iris from the printed iris information by using the cosmetic lens.
Our proposed model is based on the iris and face images in which high resolution camera and sensors are required at site to distinguish liveness Iris from fake one along with the high resolution algorithms that use combinations of DWT and SVD. Our algorithm could be used to identify the fake Iris and face and greatly improve the cyber security.
The extracted features can be obtained from videos or face images which have been captured from the scene even without individual physical contract. Our proposed model has been used for authentication and identification of the individuals based on the extracted features obtained from a new hybrid algorithm using both SVD and DWT. In proposed algorithm the face-iris features have been extracted and they are saved in the face-iris feature vectors for recognition. The Euclidian Distance vector has been used to classify the feature vectors extracted from live and fake face-iris images. Our method not only has higher speed of operation compared with other techniques but its rate of recognition is also good s as shown in the tabulated results.
Authentication and authorization architecture design for hybrid mesh networks. This paper focus on the overall security
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