Tee Connie

An automated palmprint recognition system

Recently, biometric palmprint has received wide attention from researchers. It is well-known for several advantages such as stable line features, low-resolution imaging, low-cost capturing device, and user-friendly. In this paper, an automated scanner-based palmprint recognition system is proposed. The system automatically captures and aligns the palmprint images for further processing. Several linear subspace projection techniques have been tested and compared. In specific, we focus on principal component analysis (PCA), fisher discriminant analysis (FDA) and independent component analysis (ICA). In order to analyze the palmprint images in multi-resolution-multi-frequency representation, wavelet transformation is also adopted. The images are decomposed into different frequency subbands and the best performing subband is selected for further processing. Experimental result shows that application of FDA on wavelet subband is able to yield both FAR and FRR as low as 1.356 and 1.492% using our palmprint database.

PalmHashing: a novel approach for cancelable biometrics

We propose a novel cancelable biometric approach, known as PalmHashing, to solve the non-revocable biometric issue. The proposed method hashes palmprint templates with a set of pseudo-random keys to obtain a unique code called palmhash. The palmhash code can be stored in portable devices such tokens and smartcards for verification. Multiple sets of palmhash codes can be maintained in multiple applications. Thus the privacy and security of the applications can be greatly enhanced. When compromised, revocation can also be achieved via direct replacement of a new set of palmhash code. In addition, PalmHashing offers several advantages over contemporary biometric approaches such as clear separation of the genuine-imposter populations and zero EER occurrences. In this paper, we outline the implementation details of this method and also highlight its potentials in security-critical applications.

Review article: Touch-less palm print biometrics: Novel design and implementation

In this paper, we propose an innovative touch-less palm print recognition system. This project is motivated by the publics demand for non-invasive and hygienic biometric technology. For various reasons, users are concerned about touching the biometric scanners. Therefore, we propose to use a low-resolution web camera to capture the users hand at a distance for recognition. The users do not need to touch any device for their palm print to be acquired. A novel hand tracking and palm print region of interest (ROI) extraction technique are used to track and capture the users palm in real-time video stream. The discriminative palm print features are extracted based on a new method that applies local binary pattern (LBP) texture descriptor on the palm print directional gradient responses. Experiments show promising result using the proposed method. Performance can be further improved when a modified probabilistic neural network (PNN) is used for feature matching. Verification can be performed in less than one second in the proposed system.

PalmHashing: a novel approach for dual-factor authentication

Many systems require a reliable personal authentication infrastructure to recognise the identity of a claimant before granting access to him/her. Conventional secure measures include the possession of an identity card or special knowledge like password and personal identification numbers (PINs). These methods are insecure as they can be lost, forgotten and potentially be shared among a group of co-workers for a long time without change. The fact that biometric authentication is convenient and non-refutable makes it a popular approach for a personal identification system. Nevertheless, biometric methods suffer from some inherent limitations and security threats. A more practical approach is to combine two-factor or more authenticators to achieve a higher level of security. This paper proposes a novel dual-factor authenticator based on the iterated inner product between tokenised pseudo-random numbers and user-specific palmprint features. This process generates a set of user-specific compact code called PalmHash, which is highly tolerant of data offset. There is no deterministic way to get the user-specific code without having both PalmHash and the user palmprint feature. This offers strong protection against biometric fabrication. Furthermore, the proposed PalmHashing technique is able to produce zero equal error rate (EER) and yields clean separation of the genuine and imposter populations. Hence, the false acceptance rate (FAR) can be eliminated without suffering from the increased occurrence of the false rejection rate (FRR).

A single-sensor hand geometry and palmprint verification system

Several contributions have shown that fusion of decisions or scores obtained from various single-modal biometrics verification systems often enhances the overall system performance. A recent approach of multimodal biometric systems with the use of single sensor has received significant attention among researchers. In this paper, a combination of hand geometry and palmprint verification system is being developed. This system uses a scanner as sole sensor to obtain the hands images. First, the hand geometry verification system performs the feature extraction to obtain the geometrical information of the fingers and palm. Second, the region of interest (ROI) is detected and cropped by palmprint verification system. This ROI acts as the base for palmprint feature extraction by using Linear Discriminant Analysis (LDA). Lastly, the matching scores of the two individual classifiers is fused by several fusion algorithms namely sum rule, weighted sum rule and Support Vector Machine (SVM). The results of the fusion algorithms are being compared with the outcomes of the individual palm and hand geometry classifiers. We are able to show that fusion using SVM with Radial Basis Function (RBF) kernel has outperformed other combined and individual classifiers.

An innovative contactless palm print and knuckle print recognition system

0167-8655/

A contactless biometric system using multiple hand features

see front matter 2010 Elsevier B.V. A doi:10.1016/j.patrec.2010.05.021 * Corresponding author. Tel.: +606 2523592; fax: + E-mail addresses: michael.goh@mmu.edu.my (G mmu.edu.my (T. Connie), bjteoh@yonsei.ac.kr (A. Teo This paper proposes an innovative contactless palm print and knuckle print recognition system. We present a novel palm print and knuckle print tracking approach to automatically detect and capture these features from low-resolution video stream. Besides, we introduce a simple yet robust directional coding technique to encode the palm print feature in bit string representation. The bit string representation offers speedy template matching and enables more effective template storage and retrieval. Apart from that, we present a new scheme to extract knuckle print feature via ridgelet transform. Our method is different from the others in the sense that we do not resize the knuckle print images to standard size. The scores output by the palm print and knuckle print experts are fused using Support Vector Machine. The fusion of these features yields promising result for practical implementation. 2010 Elsevier B.V. All rights reserved.

Remarks on BioHash and its mathematical foundation

With the advent of modern computing technology, there is increased reliance on biometrics to provide stronger personal authentication. Among the variety of biometric solutions in the market, hand-based system is the oldest, and perhaps the most successful form of biometric technology. This paper describes a contactless hand-based biometric system by using visible and infrared imagery. An acquisition device is developed to capture both color and infrared hand images. We modify an ordinary web camera to capture the hand vein that normally requires specialized infrared sensor. The design is simple and low-cost. No additional installation of special apparatus is required. The device can capture the epidermal and subcutaneous features from the hand simultaneously. In specific, five features namely hand geometry, palm print, palmar knuckle print, palm vein, and finger vein are acquired from the hand for recognition. Rigorous experiments had been performed to testify the robustness of the system.

Design and implementation of a contactless palm print and palm vein sensor

Although biometrics has been widely deployed in various security systems, the biometric characteristics are largely immutable, resulting in permanent biometric compromise. Cancelable biometrics was introduced [1] to denote biometric template that can be canceled and replaced, as well as is unique to every application. The cancelable biometrics issue was addressed by [2] which adopted a technique known as BioHashing. They combined the biometric template (face, fingerprint and palmprint) with a set of user-specific tokenized random numbers (TRN) to produce a set of non-invertible binary bitstrings. The revocation process was essentially the inner-product of a set of pseudo-random numbers and the biometric feature. This method delivered zero error rates when the legitimate token was used. The reported result has aroused great attention from researchers [3]. A group of researchers asserted that the outstanding performance of BioHash is actually based on the sole use of TRN, therefore they conjectured that the introduction of any forms of biometrics becomes meaningless

Robust palm print and knuckle print recognition system using a contactless approach

This paper presents an innovative contactless palm print and palm vein recognition system. We design a hand sensor that could capture the palm print and palm vein image using low-resolution web camera. Both the visible and infrared images can be captured at the same time, and we do not need specialized infrared sensor to image the vein pattern. The design of the device is simple and low-cost. The subject can be shielded completely from the complication of undergoing two separate acquiring processes. We allow subjects to position their hands freely above the sensor and they can move their hands during the acquisition process. In order to obtain clear image of the palm vascular pattern, we propose a novel image enhancement technique called local-ridge-enhancement (LRE). The proposed method removes illumination error while keeping good contrast between the print/vein pattern and the background image. Besides, we introduce a simple yet robust directional coding technique to encode the palm print and palm vein features in bit string representation. The bit string representation offers speedy template matching and enables more effective template storage and retrieval. The scores output by the palm print and palm vein experts are fused using Support Vector Machine. The fusion of these features yields promising result for practical implementation.