Shohel Sayeed

Glove-Based Approach to Online Signature Verification

Utilizing the multiple degrees of freedom offered by the data glove for each finger and the hand, a novel online 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 produce equal error rate (EER) of less than 2.37 percent.

Face recognition with Symmetric Local Graph Structure (SLGS)

Face recognition demonstrates the significant progress in the research field of biometric and computer vision. The fact is due to the current systems perform well under relatively control environments but tend to suffer when the present of variation in pose, illumination, and facial expression. In this work, a novel approach for face recognition called Symmetric Local Graph Structure (SLGS) is presented based on the Local Graph Structure (LGS). Each pixel is represented with a graph structure of its neighbours’ pixels. The histograms of the SLGS were used for recognition by using the nearest neighbour classifiers that include Euclidean distance, correlation coefficient and chi-square distance measures. AT&T and Yale face databases were used to be experimented with the proposed method. Extensive experiments on the face database clearly showed the superiority of the proposed approach over Local Binary Pattern (LBP) and LGS. The proposed SLGS is robust to variation in term of facial expressions, facial details, and illumination. Due to good performance of SLGS, it is expected that SLGS has a potential for application implementation in computer vision.

Implicit authentication based on ear shape biometrics using smartphone camera during a call

From secret knowledge like password up to physical traits as biometrics, current smartphone authentication systems are deemed inconvenience and difficult for users. Burdens on remembering password as well as privacy issues on stolen or forged biometrics have raised a futuristic idea of authentication systems. New system is hoped being transparent and with very minimum user involvement denoted as implicit authentication system. One of the ways to implicitly authenticate users is by authenticating them via image or video captured using smartphone camera during a call. During call interaction, we implicitly take ear image using front smartphone camera to recognize and authenticate users without them realizing. In this paper, we present a novel approach to ear recognition which considers both shape and texture information to represent ear image. Firstly, all Local Binary Pattern (LBP) are combined after extracted and concatenated into a single histogram. Second, in order to get geometric features, we use the idea of ear location center that is easily adjusted by smartphone user. Then, we combine previous steps to represent ear image as a descriptor. The recognition is performed using a nearest neighbor classifier computed feature space with Euclidean distance as a similarity/dissimilarity measure. Our proposed approach is very easy and simple thereby its simplicity allows very fast feature extraction. We foresee that this experiment is applicable directly on smartphone.

Forgery detection in dynamic signature verification by entailing principal component analysis.

The critical analysis of the data glove-based signature identification and forgery detection system emphasizes the essentiality of noise-free signals for input. Lucid inputs are expected for the accuracy enhancement and performance. The raw signals that are captured using 14- and 5-electrode data gloves for this purpose have a noisy and voluminous nature. Reduction of electrodes may reduce the volume but it may also reduce the efficiency of the system. The principal component analysis (PCA) technique has been used for this purpose to condense the volume and enrich the operational data by noise reduction without affecting the efficiency. The advantage of increased discernment in between the original and forged signatures using 14-electrode glove over 5-electrode glove has been discussed here and proved by experiments with many subjects. Calculation of the sum of mean squares of Euclidean distance has been used to project the advantage of our proposed method. 3.1% and 7.5% of equal error rates for 14 and 5 channels further reiterate the effectiveness of this technique.

SVD-BASED SIGNATURE VERIFICATION TECHNIQUE USING DATA GLOVE

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 paper, 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. The proposed technique is based on the Singular Value Decomposition (SVD) in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, and thus account for most of the variation in the original data, so the effective dimensionality of the data can be reduced. Having identified data glove signature through its rth principal subspace, the authenticity can then be obtained by calculating the angles between the different subspaces. The SVD-signature verification technique is tested with large number of authentic and forged signatures, showing remarkable level of accuracy in finding the similarities between genuine samples as well as those differentiated between genuine-forgery trials.

Virtual reality based dynamic signature verification using data glove

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 paper 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. The proposed technique is based on the Singular Value Decomposition (SVD) in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, and thus account for most of the variation in the original data, so the effective dimensionality of the data can be reduced. Having identified data glove signature through its r-th principal subspace, the authenticity is then can be obtained by calculating the angles between the different subspaces. The SVD-signature verification technique is tested with large number of authentic and forgery signatures and shows remarkable level of accuracy in finding the similarities between genuine samples as well as the differenced between genuine-forgery trials.

Eliminating Forgers Based on Intra Trial Variability in Online Signature Verification Using Handglove and Photometric Signals

The novel reinforcement to the data glove based dynamic signature verification system, using the Photometric measurement values collected simultaneously from photo plethysmography (PPG) during the signing process is the emerging technology. Skilled forgers try to attempt the genuine signatures in many numbers of trials. The wide gap in the Euclidian distances between forgers and the genuine template features prohibits them from successful forging. This has been proved by our repeated experiments on various subjects using the above combinational features. In addition the intra trial features captured during the forge attempts also differs widely in the case of forgers and are not consistent that of a genuine signature. This is caused by the pulse characteristics and degree of bilateral hand dimensional similarity, and the degrees of pulse delay. Since this economical and simple optical-based technology is offering an improved biometric security, it is essential to look for other reinforcements such the variability factor considerations which we proved of worth considering.

Performance Analysis of DPSK-OCDMA System for Optical Access Network

Abstract In this research, the performance of optical code division multiple access (OCDMA) using differential phase shift keying (DPSK) has been compared with OCDMA On-Off Keying (OOK). This comparison took place in terms of bit error rate (BER) and receiver power where two bit rates (155 Mbps and 622 Mbps) have been used for this analysis. Using of OptiSystem 7.0 simulation, comparing eye diagram and optical spectrum alongside with BER and Rx power. It is found that OCDMA-DPSK performs better in comparison to OCDMA-OOK. The performance analysis also provides parameter for designing and development of an OCDMA system for optical access network using DPSK.

Optimal design of wireless sensor network for providing Qos

With respect to the increase in demand of wireless sensor network in various real time Application. We present WISEN algorithm which is specially designed for WSN providing multiple communication architecture and mechanism to improve the Quality of service and routing performance. WISEN focuses on functions such as node synchronization, node localization for improving QoS. The design of WISEN algorithm evaluated through network simulator with various protocols proves better performance in terms of packet loss, throughput and delay metrics. Collectively design of WISEN assumed optimal for Researchers.

Signature verification using data glove with prominent sensors

In order to improve the security level, we have presented an innovative approach to hand signature verification using data glove. 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 research, 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. Our proposed approach is tested in context of highly skilled forgeries with a large number of signature data sets and obtained a significant level of accuracy in signature verification with 2.22%, 2.5%, 2.8% and 2.37% of Equal Error Rates (EER) with 5-, 7-, 9-, and 14-sensor based data sets, respectively.