Automatic Signature Verification (ASV) in e-commerce

In the offline world, payments are often made over the counter with some levels of human inspections. However,such inspection does not exist in the online world. Online transactions are carried out virtually on a remote application server. Though other technical security measures have been introduced for online transactions such as the use of encryption, digital signatures and digital certificates, the issue of trust is still largely a major problem in e-commerce since actual authentication of users is not often established. A solution to this is to use biometrics Automatic Signature Verification (ASV) systems where human identification is carried out automatically based on their signatures.The main advantage of ASV over other biometrics technologies is that its applications are widely accepted and generally acknowledged by the public due to the fact that signatures have long been used as proof of identity in legal documents and financial transactions.Additionally, the ASV system allows the extraction of dynamic information that describes the way a signature is actually executed in terms of velocity, acceleration, pen pressure, pen inclination, etc. Many signature experts believe the dynamic information of the signing operation is generally consistent and stable throughout one’s lifetime.This in turn is more secure simply because it is harder to imitate human signing operation than to reproduce signature images of another person.Since ASV allows for remote networked authentication, it appears promising to most e-commerce applications. This paper generally describes the ASV potentials, its current applications and impediments in e-commerce related activities.It also addresses areas for ASV improvements

Statistical analysis of image quality measures for face liveness detection

Face recognition is essential for a wide range of technologies that requires person identification. Due to the presence of spoof face attacks, an additional layer of security is needed to protect the system, which can be provided by liveness detection. In this paper we develop a technique for discriminating live from fake images. Our approach is based upon the hypothesis that spoofing scheme leave statistical indication or structure in images which can be utilized for detection by assistance of image quality features. To achieve this, image quality measures (IQMs) statistical evaluation has been implemented using the analysis of variance (ANOVA) technique. A feature set of measures with highest discrimination power to distinguish between real and fake images was obtained. This ensures the simplicity of detection system and improves its computational efficiency

A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: Study case on Malay word recognition

An off-line handwriting recognition (OFHR) system is a computerized system that is capable of intelligently converting human handwritten data extracted from scanned paper documents into an equivalent text format. This paper studies a proposed OFHR for Malaysian bank cheques written in the Malay language. The proposed system comprised of three components, namely a character recognition system (CRS), a hybrid decision system and lexical word classification system. Two types of feature extraction techniques have been used in the system, namely statistical and geometrical. Experiments show that the statistical feature is reliable, accessible and offers results that are more accurate. The CRS in this system was implemented using two individual classifiers, namely an adaptive multilayer feed-forward back-propagation neural network and support vector machine. The results of this study are very promising and could generalize to the entire Malay lexical dictionary in future work toward scaled-up applications

Robotic cans surface inspection system based on shape features

Computer vision systems are one of the most widely used techniques in Automation and have been extensively used for industry automation. Industrial automation deals mainly with the automation of production, quality control and materials management processes. One trend is the increasing use of Machine vision to offer automatic inspection and robot guidance functions, while the other is a continued increase in the use of robots. The aim of this paper is to provide a robotic cans surface inspection system based on the shape. The proposed system is simple and user friendly yet accurate, uses Hu moment as a feature of detected shape in the image and compared to the range of acceptable Hu moment gained from training. It is composed of a camera attached to a PC with TCP/IP, image acquisition, analysis, and inspection implemented by Open CV Library for image processing. The method described in this paper checks on the statistical-based approaches for feature extraction such as moment feature as part of the final inspection system. Robotic arm is programed as a client server method to receive action and position from the PC, which carries out the image processing as well

A multi-purpose watermarking scheme based on hybrid of lifting wavelet transform and Arnold transform

This paper introduces a new multi-purpose image watermarking algorithm which based on a hybrid of lifting wavelet transform (LWT) and Arnold transform for copyright protection and image authentication. In the proposed scheme, the original image is first decomposed by LWT into four subbands. Then the robust watermark which is a binary logo image is decomposed by Discrete Wavelet Transform (DWT) as such only the high frequency subband of the watermark are embedded in the low frequency subband of the original image. The fragile watermark is block wise self-generated from the original image and scrambled using Arnold transform which is later embedded in the spatial domain of the robust watermarked image. Self-generated fragile watermark supports self-authentication with high localization, whereas scrambling the fragile watermark increases the security of the algorithm. On the other hand, the lifting scheme approaches have almost one half the amounts of operations compared to the DWT based approaches. The overall system has been tested against various attacks and the results demonstrated that the robust watermark can be decoded successively under each attack. In addition, the proposed algorithm can detect any tampering attempts

Analysis of the effects and relationship of perceived handwritten signature's size, graphical complexity, and legibility with dynamic parameters for forged and genuine samples

This article presents an analysis of handwritten signature dynamics belonging to two authentication groups, namely genuine and forged signature samples. Genuine signatures are initially classified based on their relative size, graphical complexity, and legibility as perceived by human examiners. A pool of dynamic features is then extracted for each signature sample in the two groups. A two-way analysis of variance (ANOVA) is carried out to investigate the effects and the relationship between the perceived classifications and the authentication groups. Homogeneity of variance was ensured through Bartlett's test prior to ANOVA testing. The results demonstrated that among all the investigated dynamic features, pen pressure is the most distinctive which is significantly different for the two authentication groups as well as for the different perceived classifications. In addition, all the relationships investigated, namely authenticity group versus size, graphical complexity, and legibility, were found to be positive for pen pressure

FPGA implementation of variable precision Euclid’s GCD algorithm

Introduction: Euclid's algorithm is well-known for its efficiency and simple iterative to compute the greatest common divisor (GCD) of two non-negative integers. It contributes to almost all public key cryptographic algorithms over a finite field of arithmetic. This, in turn, has led to increased research in this domain, particularly with the aim of improving the performance throughput for many GCD-based applications. Methodology: In this paper, we implement a fast GCD coprocessor based on Euclid's method with variable precisions (32-bit to 1024-bit). The proposed implementation was benchmarked using seven field programmable gate arrays (FPGA) chip families (i.e., one Altera chip and six Xilinx chips) and reported on four cost complexity factors: the maximum frequency, the total delay values, the hardware utilization and the total FPGA thermal power dissipation. Results: The results demonstrated that the XC7VH290T-2-HCG1155 and XC7K70T-2-FBG676 devices recorded the best maximum frequencies of 243.934 MHz down to 39.94 MHz for 32-bits with 1024-bit precisions, respectively. Additionally, it was found that the implementation with different precisions has utilized minimal resources of the target device, i.e., a maximum of 2% and 4% of device registers and look-up tables (LUT’s). Conclusions: These results imply that the design area is scalable and can be easily increased or embedded with many other design applications. Finally, comparisons with previous designs/implementations illustrate that the proposed coprocessor implementation is faster than many reported state-of-the-art solutions. This paper is an extended version of our conference paper [1]

Real-time pen input system for writing utilizing stereo vision

A system that captures handwritten words on a piece of paper utilizing two cameras which observe and track a pen’s tip lateral movement is presented. The system tracks the pen’s tip in real-time based on the two images vision using colored markers. The stereo camera is calibrated for accurate threedimensional (3D) positioning of the pen’s tip. Pen’s tip 3D coordinates are then being used to re-construct the handwritten input into computer image. Experimental results show that the system can detect and track handwriting within 1mm accuracy on the y-axis and 7 mm accuracy on the x-axis (depth)

Soft biometrics: gender recognition from unconstrained face images using local feature descriptor

Gender recognition from unconstrained face images is a challenging task due to the high degree of misalignment, pose, expression, and illumination variation. In previous works, the recognition of gender from unconstrained face images is approached by utilizing image alignment, exploiting multiple samples per individual to improve the learning ability of the classifier, or learning gender based on prior knowledge about pose and demographic distributions of the dataset. However, image alignment increases the complexity and time of computation, while the use of multiple samples or having prior knowledge about data distribution is unrealistic in practical applications. This paper presents an approach for gender recognition from unconstrained face images. Our technique exploits the robustness of local feature descriptor to photometric variations to extract the shape description of the 2D face image using a single sample image per individual. The results obtained from experiments on Labeled Faces in the Wild (LFW) dataset describe the effectiveness of the proposed method. The essence of this study is to investigate the most suitable functions and parameter settings for recognizing gender from unconstrained face images

A study on multipurpose watermarking techniques for image

Conventional single watermark systems are mainly aimed at accomplishing a single goal, either for forgery detection or image copyright protection. This limitation has resulted in the introduction of multipurpose or otherwise known as multifunction watermarking algorithms, with the prime objective of simultaneously achieving both goals. Research in this domain has attracted tremendous interest in recent years, mainly due to its challenging nature in effectively satisfying both aims without degrading one another. However, most of the recent studies have not indicated a clear distinction between multipurpose and multiple watermarks (or cocktail watermarking) algorithms. This paper differentiates between these two types of digital watermarking systems and focuses on multipurpose watermarking due to its significance. In addition, it presents a state of the art survey on the theories, models, features, and algorithms that have been implemented in designing a multipurpose watermarking algorithm. It highlights the recent trends in related techniques and most reliable results attained, whilst also pointing the possible future research directions that can be investigated