Goh Kah Ong Michael

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.

An innovative contactless palm print and knuckle print recognition system

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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.

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

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.

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.

A Contactless Biometric System Using Palm Print and Palm Vein Features

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. No constraint is imposed and the subject can place his/her hand naturally on top of the sensor without touching any device. Besides, we introduce a simple yet robust directional coding technique to encode the palm print feature in bit string representation. In addition, a new scheme is presented to extract knuckle print feature using 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 geometrical information of the knuckle print can thus be retained and utilized in our work. Support Vector Machine was used to fuse the scores output by the palm print and knuckle print experts. The fusion of these features yields promising result for real-time application.

Grassmannian locality preserving discriminant analysis to view invariant gait recognition with image sets

Recently, biometrics has emerged as a reliable technology to provide greater level of security to personal authentication system. Among the various biometric characteristics that can be used to recognize a person, the human hand is the oldest, and perhaps the most successful form of biometric technology (Hand-based biometrics, 2003). The features that can be extracted from the hand include hand geometry, fingerprint, palm print, knuckle print, and vein. These hand properties are stable and reliable. Once a person has reached adulthood, the hand structure and configuration remain relatively stable throughout the person’s life (Yoruk et al., 2006). Apart from that, the hand-scan technology is generally perceived as nonintrusive as compared to irisor retina-scan systems (Jain et al., 2004). The users do not need to be cognizant of the way in which they interact with the system. These advantages have greatly facilitated the deployment of hand features in biometric applications. At present, most of the hand acquisition devices are based on touch-based design. The users are required to touch the device or hold on to some peripheral or guidance peg for their hand images to be captured. There are a number of problems associated with this touchbased design. Firstly, people are concerned about the hygiene issue in which they have to place their hands on the same sensor where countless others have also placed theirs. This problem is particularly exacerbated during the outbreak of epidemics or pandemics like SARS and Influenza A (H1N1) which can be spread by touching germs leftover on surfaces. Secondly, latent hand prints which remain on the sensor’s surface could be copied for illegitimate use. Researchers have demonstrated systematic methods to use latent fingerprints to create casts and moulds of the spoof fingers (Putte & Keuning, 2000). Thirdly, the device surface will be contaminated easily if not used right, especially in harsh, dirty, and outdoor environments. Lastly, some nations may resist placing their hands after a user of the opposite sex has touched the sensor. This chapter presents a contactless hand-based biometric system to acquire the palm print and palm vein features. Palm prints refer to the smoothly flowing pattern formed by alternating creases and troughs on the palmar surface of the hand. Three types of line patterns are clearly visible on the palm. These line patterns are known as the principal lines, wrinkles, and ridges. Principal lines are the longest, strongest and widest lines on the palm. The principal lines characterize the most distinguishable features on the palm. Most people have three principal lines, which are named as the heart line, head line, and life line (Fig. 1).

Design and implementation of a contactless palm vein recognition system

In studies to date, gait recognition across appearance changes has been a challenging task. In this paper, we present a gait recognition method that models the gait image sets as subspaces on the Grassmannian manifold. This formulation provides a convenient way to represent the subspaces as points on the manifold. By using a suitable Grassmannian kernel, the non-linear manifold can be treated as if it were a Euclidean space. This implies that conventional data analysis tool like LDA can be used on this manifold. To this end, we apply a graph based locality preserving discriminant analysis method on the Grassmannian manifold. Experiment results suggest that the proposed method can tolerate variations in appearance for gait identification.In studies to date, gait recognition across appearance changes has been a challenging task. In this paper, we present a gait recognition method that models the gait image sets as subspaces on the Grassmannian manifold. This formulation provides a convenient way to represent the subspaces as points on the manifold. By using a suitable Grassmannian kernel, the non-linear manifold can be treated as if it were a Euclidean space. This implies that conventional data analysis tool like LDA can be used on this manifold. To this end, we apply a graph based locality preserving discriminant analysis method on the Grassmannian manifold. Experiment results suggest that the proposed method can tolerate variations in appearance for gait identification.

Locating geometrical descriptors for hand biometrics in a contactless environment

This paper presents an innovative contactless palm vein recognition system. We design a hand sensor that could capture the palm vein image using low-resolution web camera. The design of the sensor is simple and low-cost, and we do not need to install specialized infrared sensor. 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 processing technique called local-ridge-enhancement (LRE). The proposed method removes illumination error while keeping good contrast between the vein and the surrounding tissue. Apart from that, we present a new way to evaluate the image quality. Sometimes, the vein image does not appear clear due to the medical condition of the skin (like thick fatty tissue obstructing the subcutaneous blood vessels) and other environmental factor. These types of images are not suitable to be used for processing in our system. Therefore, we introduce the image quality checking procedure to evaluate the quality of the image before accepting it into our system. The proposed methodology improves the overall performance of the palm vein recognition system.

A preliminary acclimatization study of a contactless biometrics using palm vein feature

This paper proposes an innovative contactless hand geometry recognition system. We present a novel hand tracking approach to automatically detect and capture the geometrical features of the hand from low resolution video stream. No constraint is imposed and the subject can place his/her hand naturally on top of the sensor without touching any device. Conventional hand geometry systems require fairly precise positioning of the hand in order to obtain accurate measures of the hand. However, the proposed contactless approach does not fix any guidance pegs to help placing the hand at the right position when the image is acquired. As a result, the hand image may appear larger when the hand is placed near the sensor, and vice versa. Besides, the hand can be positioned at different angles. In other words, there is no way to obtain standard and constant hand measurements from this contactless setting. This research aims to deal with this complication when we have to get accurate measurements of the hand from images with varying sizes and directed at different orientations. Experiments show that our proposed method offers promising result for hand geometry recognition in a real-time contactless environment.