Wan Azizun Wan Adnan

Integration of multiple soft biometrics for human identification

A new local feature descriptor is proposed for facial shape representation.The performance on still and surveillance face datasets is comparable to the state of the arts.We present experimental findings on integration of face and body based soft biometrics.Five score fusion techniques are examined to determine the most reliable method.Fuzzy logic is discovered as the most effective score fusion. We propose a computational approach to human identification based on the integration of face and body related soft biometric traits. In previous studies on soft biometrics, several methods for human identification using semantic descriptions have been introduced. Though the results attained exhibit the effectiveness of such techniques in image retrieval and short term tracking of subjects, semantics literally limits the ability of a biometric system to provide conclusive identification. This paper presents a new framework for biometric identification based solely on multiple measured soft biometric traits. The paper describes techniques for extracting/estimating face and body based soft biometric traits from frame set. Furthermore, we utilized a sequential attribute combination method to perform attribute selection prior to integration at match score level. Finally, an evaluation of five score fusion techniques is performed. The results show that the proposed framework can be utilized to model an adequate soft biometric system with rank-1 identification rate of 88%. Display Omitted

Online Handwritten Signature Verification Using Neural Network Classifier Based on Principal Component Analysis

One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%.

Recent advances in facial soft biometrics

Face as a biometric attribute has been extensively studied over the past few decades. Even though, satisfactory results are already achieved in controlled environments, the practicality of face recognition in realistic scenarios is still limited by several challenges, such as, expression, pose, occlusion, etc. Recently, the research direction is concentrating on the prospects of complementing face recognition systems with facial soft biometric traits. The ease of extracting facial soft biometrics under several varying conditions has mainly resulted in the ability of using the traits to, either improve the performance of traditional face recognition systems, or performing recognition solely based on many facial soft biometrics. This paper presents state-of-the-art techniques in facial soft biometrics research by describing the type of traits, feature extraction methods, and the application domains. It indicates the most recent and valuable results attained, while also highlighting some possible future scientific research directions to be investigated.

Analysis of watermarking techniques in video

Video piracy has become an increasing problem particularly with the proliferation of media sharing through the advancement of internet services and various storage technologies. Thus, research in copyright protection mechanisms, where one of which includes digital watermarking has been receiving an increasing interest from scientists especially in designing a seamless algorithm for effective implementation. Basically digital watermarking involves embedding secret symbols known as watermarks within video data which can be used later for copyright detection purposes. This paper presents the state of the art in video watermarking techniques. It provides a critical review on various available techniques. In addition, it addresses the main key performance indicators which include robustness, speed, capacity, fidelity, imperceptibility and computational complexity.

A review of image watermarking

Editing, reproduction and distribution of the digital multimedia are becoming extremely easier and faster with the existence of the Internet and the availability of pervasive and powerful multimedia tools. However, these advances have their drawbacks as well, for example unauthorized tampering of images. Digital watermarking has emerged as a possible method to tackle these issues. A digital watermark is created by inserting a digital signal, or pattern within multimedia content. This embedded information can be used to determine whether the host data are being tampered with or not. This paper presents a review of watermarking where watermarking processes are described briefly and the appropriate properties for different applications are discussed.

Gender recognition on real world faces based on shape representation and neural network

Gender as a soft biometric attribute has been extensively investigated in the domain of computer vision because of its numerous potential application areas. However, studies have shown that gender recognition performance can be hindered by improper alignment of facial images. As a result, previous experiments have adopted face alignment as an important stage in the recognition process, before performing feature extraction. In this paper, the problem of recognizing human gender from unaligned real world faces using single image per individual is investigated. The use of feature descriptor to form shape representation of face images with any arbitrary orientation from the cropped version of Labeled Faces in the Wild (LFW) dataset is proposed. By combining the feature extraction technique with artificial neural network for classification, a recognition rate of 89.3% is attained.

Estimating body related soft biometric traits in video frames

Soft biometrics can be used as a prescreening filter, either by using single trait or by combining several traits to aid the performance of recognition systems in an unobtrusive way. In many practical visual surveillance scenarios, facial information becomes difficult to be effectively constructed due to several varying challenges. However, from distance the visual appearance of an object can be efficiently inferred, thereby providing the possibility of estimating body related information. This paper presents an approach for estimating body related soft biometrics; specifically we propose a new approach based on body measurement and artificial neural network for predicting body weight of subjects and incorporate the existing technique on single view metrology for height estimation in videos with low frame rate. Our evaluation on 1120 frame sets of 80 subjects from a newly compiled dataset shows that the mentioned soft biometric information of human subjects can be adequately predicted from set of frames.

Linux Support for Fast Transparent General Purpose Checkpoint/Restart of Multithreaded Processes in Loadable Kernel Module

Checkpoint/Restart is the ability to save the state of a running application so that it can later resume its execution from the time of the checkpoint. These are techniques with many potential applications, including establishment of a fault-tolerant environment, improving system resource utilization, and true migration of a process. With increasing hardware speed and size of clusters the average time between failures has been reduced. Therefore, fault tolerance and ability to checkpoint a process have become inevitable. Almost all platforms deployed for high-performance computing support process checkpoint/restart. Linux as one of the popular operating systems does not provide a general purpose implementation. Some are limited to specific type of parallel programming library, confined to some unique well-behaved type of applications, or reliant on specific features in kernel which could be missing on many occasions. Most of implementations demand elaborate practice of recompiling a whole kernel to apply required patches. In this paper, we describe the design and implementation of multithreaded process checkpoint/restart system for Linux which provide capability of dynamic extension to increase compatibility and reduce system overhead. It does not impose any requirement on the existence of a special facility in the operating system and can do checkpoint/restart of an application independent of their behavior and fully transparent. The entire system is absolutely implemented in multiple kernel loadable modules, which result in ease of use and eliminate the burden of complex system administration.

Characteristics of multihole collimator gamma camera simulation modeled using MCNP5

This paper describes the characteristics of the multihole collimator gamma camera that is simulated using the combination of the Monte Carlo N‐Particles Code (MCNP) version 5 and in‐house software. The model is constructed based on the GCA‐7100A Toshiba Gamma Camera at the Royal Surrey County Hospital, Guildford, Surrey, UK. The characteristics are analyzed based on the spatial resolution of the images detected by the Sodium Iodide (NaI) detector. The result is recorded in a list‐mode file referred to as a PTRAC file within MCNP5. All pertinent nuclear reaction mechanisms, such as Compton and Rayleigh scattering and photoelectric absorption are undertaken by MCNP5 for all materials encountered by each photon. The experiments were conducted on Tl‐201, Co‐57, Tc‐99 m and Cr‐51 radio nuclides. The comparison of full width half maximum value of each datasets obtained from experimental work, simulation and literature are also reported in this paper. The relationship of the simulated data is in agreement with the experimental results and data obtained in the literature. A careful inspection at each of the data points of the spatial resolution of Tc‐99 m shows a slight discrepancy between these sets. However, the difference is very insignificant, i.e. less than 3 mm only, which corresponds to a size of less than 1 pixel only (of the segmented detector).

Online signature verification using neural network and pearson correlation features

In this paper, we proposed a method for feature extraction in online signature verification. We first used signature coordinate points and pen pressure of all signatures, which are available in the SIGMA database. Then, Pearson correlation coefficients were selected for feature extraction. The obtained features were used in back-propagation neural network for verification. The results indicate an accuracy of 82.42%.