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Utilizing the multiple degrees of freedom offered by the data glove for each finger and the hand, a novel on-line signature verification system using the Singular Value Decomposition (SVD) numerical tool for signature classification and verification is presented. The proposed technique is based on the Singular Value Decomposition in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, so the effective dimensionality of A can be reduced. Having modeled the data glove signature through its r-principal subspace, signature authentication is performed by finding the angles between the different subspaces. A demonstration of the data glove is presented as an effective high-bandwidth data entry device for signature verification. This SVD-based signature verification technique is tested and its performance is shown to be able to recognize forgery signatures with a false acceptance rate of less than 1.2%.

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With the pace of increasing online transactions and communication, the demand for security and privacy increases. To protect confidential information and to authenticate people electronically, several solutions already introduced. Traditional biometric systems that are based on single biometric usually suffer from problems like impostors' attack or hacking, unacceptable error rates. To improve security and privacy and system's reliability two or more biometrics of the same identity could be combined in a method that enhances the efficiency of the system. The biometric information, however, is irreplaceable information, when it is compromised. Thereby, one must give a special attention to protection of such information. We propose a novel protection technique for the biometric information, especially the feature information and the templates. The point of our proposal is securely embeds and extracts an iris template in a fingerprint image using a combined DWT and LSB based biometric watermarking algorithm in each authentication. The embedded data travel through insecure communication line like the internet, and they are used in matching process. This technique causes security against eavesdropping and replay attacks on the internet, because the watermark embedded transmitted data are used in the authentication session after watermark extraction.

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Data glove is a new dimension in the field of virtual reality environments, initially designed to satisfy the stringent requirements of modern motion capture and animation professionals. In this study we try to shift the implementation of data glove from motion animation towards signature verification problem, making use of the offered multiple degrees of freedom for each finger and for the hand as well. We used an SVD-based technique to extract the feature values of different sensors locating on corresponding fingers in the signing process and evaluated the results for writer authentication. The technique is tested with large number of authentic and forgery signatures using data gloves with 14, 5 and 4 sensor and shows a significant level of accuracy with 2.46~5.0% of EER

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