Thian Song Ong

Bio: Thian Song Ong is an academic researcher from Multimedia University. The author has contributed to research in topics: Biometrics & Computer science. The author has an hindex of 16, co-authored 58 publications receiving 733 citations.

Fingerprint template protection with minutiae-based bit-string for security and privacy preserving

Abstract: Recently, biometric template protection has received great attention from the research community due to the security and privacy concerns for biometric template. Although a number of biometric template protection methods have been reported, it is still a challenging task to devise a scheme which satisfies all of the four template protection criteria namely diversity, revocability, non-invertibility and performance. In this paper, a method is proposed to generate a revocable fingerprint template in terms of bit-string from a set of minutiae points via a polar grid based 3-tuple quantization technique. Two merits of the proposed method are outlined, namely alignment-free and performance. Four publicly available benchmark datasets: FVC2002 DB1, DB2 and FVC2004 DB1, DB2 are used to evaluate the performance of the proposed method. Besides, the diversity, revocability, non-invertibility criteria are also analyzed.

Integrated biometrics template protection technique based on fingerprint and palmprint feature-level fusion

Abstract: Multi-biometric systems are known to be universal and more accurate in biometric recognition. However, the storage of multiple biometric templates as separate entities pose major threats to user privacy and system security. Therefore, we propose to fuse multiple biometric modalities at the feature level in order to obtain an integrated template and to secure the fused templates using a hybrid template protection method. The proposed method is made out of a feature transformation technique known as Random Tiling and an equal-probable 2^N discretization scheme. The former enables the revocability of the template and the latter converts the feature elements into binary representations according to the area under the genuine interval curve, in order to offer better privacy protection to the template. Our experimental results show that the proposed multi-biometric template protection scheme demonstrates better verification results as compared to their unibiometric counterparts while preserving template security.

Enhanced maximum curvature descriptors for finger vein verification

Abstract: Maximum Curvature Method (MCM) is one of the promising methods for finger vein verification. MCM scans the curvature of the vein image profiles within a finger for feature extraction. However, the quality of the image can be poor due to variations in illumination and sensor conditions. Furthermore, traditional MCM matching of the vein pattern requires extensive processing time. To address these limitations, we propose an integrated Enhanced Maximum Curvature (EMC) method with Histogram of Oriented Gradient (HOG) descriptor for finger vein verification. Unlike MCM, EMC incorporates an enhancement mechanism to extract small vein delineation that is hardly visible in the extracted vein patterns. Next, HOG is applied instead of image binarization to convert a two-dimensional vein image into a one-dimensional feature vector for efficient matching. The HOG descriptor is able to characterize the local spatial representation of a finger vein by capturing the gradient information effectively. The proposed method is evaluated based on two datasets namely the PKU Finger Vein Database (V4) and SDUMLA-HMT finger vein database. Experiments show promising verification results with equal error rates as low as 0.33 % for DB1 and 0.14 % for DB2 respectively, when EMC+HOG+SVM is applied.

Statistical Fusion Approach on Keystroke Dynamics

Abstract: Keystroke dynamics refers to a userpsilas habitual typing characteristics. These typing characteristics are believed to be unique among large populations. In this paper, we present a novel keystroke dynamic recognition system by using a fusion method. Firstly,we record the dwell time and the flight time as the feature data. We then calculate their mean and standard deviation values and stored. The test feature data will be transformed into the scores via Gaussian probability density function. On the other hand, we also propose a new technique, known as Direction Similarity Measure (DSM) to measure the differential of sign among each coupled characters in a phrase. Lastly, a weighted sum rule is applied by fusing the Gaussian scores and the DSM to enhance the final result. The best result of equal error rate 6.36% is obtained by using our home-made dataset.

Keystroke dynamics in password authentication enhancement

Abstract: This paper describes a novel technique to strengthen password authentication system by incorporating multiple keystroke dynamic information under a fusion framework. We capitalize four types of latency as keystroke feature and two methods to calculate the similarity scores between the two given latency. A two layer fusion approach is proposed to enhance the overall performance of the system to achieve near 1.401% Equal Error Rate (EER). We also introduce two additional modules to increase the flexibility of the proposed system. These modules aim to accommodate exceptional cases, for instance, when a legitimate user is unable to provide his or her normal typing pattern due to reasons such as hand injury.

Parallel Optimization: Theory, Algorithms and Applications

Abstract: Yair Censor and Stavros A. Zenios, Oxford University Press, New York, 1997, 539 pp., ISBN 0-19-510062-X, $85.00

Biometric Authentication and Identification using Keystroke Dynamics: A Survey

Abstract: Dependence on computers to store and process sensitive information has made it necessary to secure them from intruders. A behavioral biometric such as keystroke dynamics which makes use of the typing cadence of an individual can be used to strengthen existing security techniques effectively and cheaply. Due to the ballistic (semi-autonomous) nature of the typing behavior it is difficult to impersonate, making it useful as abiometric. Therefore in this paper, we provide a basic background of the psychological basis behind the use of keystroke dynamics. We also discuss the data acquisition methods, approaches and the performance of the methods used by researchers on standard computer keyboards. In this survey, we find that the use and acceptance of this biometric could be increased by development of standardized databases, assignment of nomenclature for features, development of common data interchange formats, establishment of protocols for evaluating methods, and resolution of privacy issues.

A survey of keystroke dynamics biometrics

Abstract: Research on keystroke dynamics biometrics has been increasing, especially in the last decade. The main motivation behind this effort is due to the fact that keystroke dynamics biometrics is economical and can be easily integrated into the existing computer security systems with minimal alteration and user intervention. Numerous studies have been conducted in terms of data acquisition devices, feature representations, classification methods, experimental protocols, and evaluations. However, an up-to-date extensive survey and evaluation is not yet available. The objective of this paper is to provide an insightful survey and comparison on keystroke dynamics biometrics research performed throughout the last three decades, as well as offering suggestions and possible future research directions.

Review Article: Biometric personal authentication using keystroke dynamics: A review

Abstract: Authentication is the process of determining whether someone or something is, in fact, who or what it is declared to be. As the dependence upon computers and computer networks grows, the need for authentication has increased. Biometrics is the science and technology of authentication by identifying the living individual's physiological or behavioral attributes. Keystroke dynamics is a behavioral measurement and it utilizes the manner and rhythm in which each individual types. The approaches in keystroke dynamics can be categorized by the selection of features and the classification methods employed. The objective of this review paper is to summarize the well-known approaches used in keystroke dynamics in the last two decades.

Dual-source discrimination power analysis for multi-instance contactless palmprint recognition

Abstract: Due to the benefits of palmprint recognition and the advantages of biometric fusion systems, it is necessary to study multi-source palmprint fusion systems. Unfortunately, the research on multi-instance palmprint feature fusion is absent until now. In this paper, we extract the features of left and right palmprints with two-dimensional discrete cosine transform (2DDCT) to constitute a dual-source space. Normalization is utilized in dual-source space to avoid the disturbance caused by the coefficients with large absolute values. Thus complicated pre-masking is needless and arbitrary removing of discriminative coefficients is avoided. Since more discriminative coefficients can be preserved and retrieved with discrimination power analysis (DPA) from dual-source space, the accuracy performance is improved. The experiments performed on contactless palmprint database confirm that dual-source DPA, which is designed for multi-instance palmprint feature fusion recognition, outperforms single-source DPA.