Personal Authentication Using Finger Knuckle Surface

This paper investigates a new approach for personal authentication using fingerback surface imaging. The texture pattern produced by the finger knuckle bending is highly unique and makes the surface a distinctive biometric identifier. The finger geometry features can be simultaneously acquired from the same image at the same time and integrated to further improve the user-identification accuracy of such a system. The fingerback surface images from each user are normalized to minimize the scale, translation, and rotational variations in the knuckle images. This paper details the development of such an approach using peg-free imaging. The experimental results from the proposed approach are promising and confirm the usefulness of such an approach for personal authentication.      

A novel framework for automatic 3D face recognition using quality assessment

The quality of biometric samples plays an important role in biometric authentication systems because it has a direct impact on verification or identification performance. In this paper, we present a novel 3D face recognition system which performs quality assessment on input images prior to recognition. More specifically, a reject option is provided to allow the system operator to eliminate the incoming images of poor quality, e.g. failure acquisition of 3D image, exaggerated facial expressions, etc.. Furthermore, an automated approach for preprocessing is presented to reduce the number of failure cases in that stage. The experimental results show that the 3D face recognition performance is significantly improved by taking the quality of 3D facial images into account. The proposed system achieves the verification rate of 97.09% at the False Acceptance Rate (FAR) of 0.1% on the FRGC v2.0 data set.     

基于曲线拟合的手形生物特征认证新算法

针对手形的特点和现有手形认证方法的不足,提出了一种基于曲线拟合的手形生物特征认证新算法．该算法使用手指轮廓拟合曲线的系数作为手形的特征,使用曲线距离函数进行匹配认证,进一步导出基于曲线系数进行求解的简化方法．实验表明,该算法的认证错误接收率和错误拒绝率之和达到1％以下;与现有的手形认证方法相比,该算法在认证的准确率、鲁棒性和运算量方面具有良好的综合性能．     

Generating cancelable fingerprint templates

Biometrics-based authentication systems offer obvious usability advantages over traditional password and token-based authentication schemes. However, biometrics raises several privacy concerns. A biometric is permanently associated with a user and cannot be changed. Hence, if a biometric identifier is compromised, it is lost forever and possibly for every application where the biometric is used. Moreover, if the same biometric is used in multiple applications, a user can potentially be tracked from one application to the next by cross-matching biometric databases. In this paper, we demonstrate several methods to generate multiple cancelable identifiers from fingerprint images to overcome these problems. In essence, a user can be given as many biometric identifiers as needed by issuing a new transformation "key". The identifiers can be cancelled and replaced when compromised. We empirically compare the performance of several algorithms such as Cartesian, polar, and surface folding transformations of the minutiae positions. It is demonstrated through multiple experiments that we can achieve revocability and prevent cross-matching of biometric databases. It is also shown that the transforms are noninvertible by demonstrating that it is computationally as hard to recover the original biometric identifier from a transformed version as by randomly guessing. Based on these empirical results and a theoretical analysis we conclude that feature-level cancelable biometric construction is practicable in large biometric deployments     

Identifying Noncooperative Subjects at a Distance Using Face Images and Inferred Three-Dimensional Face Models

We present an approach to identify noncooperative individuals at a distance from a sequence of images, using 3-D face models. Most biometric features (such as fingerprints, hand shape, iris, or retinal scans) require cooperative subjects in close proximity to the biometric system. We process images acquired with an ultrahigh-resolution video camera, infer the location of the subjects' head, use this information to crop the region of interest, build a 3-D face model, and use this 3-D model to perform biometric identification. To build the 3-D model, we use an image sequence, as natural head and body motion provides enough viewpoint variation to perform stereomotion for 3-D face reconstruction. We have conducted experiments on a 2-D and 3-D databases collected in our laboratory. First, we found that metric 3-D face models can be used for recognition by using simple scaling method even though there is no exact scale in the 3-D reconstruction. Second, experiments using a commercial 3-D matching engine suggest the feasibility of the proposed approach for recognition against 3-D galleries at a distance (3, 6, and 9 m). Moreover, we show initial 3-D face modeling results on various factors including head motion, outdoor lighting conditions, and glasses. The evaluation results suggest that video data alone, at a distance of 3 to 9 meters, can provide a 3-D face shape that supports successful face recognition. The performance of 3-D–3-D recognition with the currently generated models does not quite match that of 2-D–2-D. We attribute this to the quality of the inferred models, and this suggests a clear path for future research.      

A survey of biometric technology based on hand shape

Automated biometric systems have emerged as a more reliable alternative to the traditional personal identification solutions. One of the most popular biometrics is hand shape due to its ease of use, non-intrusiveness and public acceptance. This paper presents a survey of the technology used in hand shape-based biometric systems. We first review the component modules including the algorithms they employ. Next we discuss system taxonomies, performance evaluation methodologies, testing issues and US government evaluations. A summary of the accuracy results reported in the literature is also provided. We next describe some of the commercial hand shape biometric systems as well as some recent successful deployments. Finally, we mention a few limitations of the hand shape biometric and give some directions for future research.     

Merging face and finger images for human identification

Human identification performance reported so far using face or finger images under certain conditions is good practice, however, there is still a great need for better performance in biometrics for use in video surveillance. One possible way to achieve improved performance is to combine information from multiple sources. Besides, such systems alleviate some of the problems that are faced by single biometrics-based systems like restricted degrees of freedom, spoof attacks, and unacceptable error rates. We present a prototype bimodal biometric identification system by merging face and finger images. A novel approach is adopted to merge biometric (face and finger) traits of an individual to one image (containing features of both), named merged pattern. The integrated features are then extracted with an adaptive artificial neural network. The proposed algorithm is shown to exhibit robustness in achieving better classification results with both good generalization performance and a fast training/test time using variable public domain databases. Sources of variability include facial expression, gender, individual appearance, tilt, lighting conditions, and occluding objects (hair, spectacles, etc).     

A MULTIMODAL BIOMETRIC IDENTIFICATION SYSTEM USING COMPRESSED FINGER IMAGES

Finger image recognition remains one of the most prominent biometric identification methods. However, storage of finger databases needs allocation of huge secondary storage devices. In addition, there has been limited success in obtaining a satisfactory system due to the complexity of the problem. In this article, a low-cost, high-speed, multimodal biometric identification system trained with compressed finger images is presented with the objective to increase the overall matching confidence level. For this, three finger images of the left or right hand, or both, for one person are matched and the output decision is combined. A prototype optical-based finger-data acquisition system using the CCD (charge coupled device) digital still camera is adopted to capture a complete impression of finger area required for accurately identifying an individual. The acquired images then are compressed with a Coif5 wavelet packet-based scheme to increase the overall performance and eliminate bulk storage requirements. The finger image features are extracted with an adaptive neural network for the implementation of a three-finger multimodal system to achieve a peak identification rate of 100% (99.4% on average) in 0.15s for a database of 50 persons and 450 test images.     

A biometric identification system based on eigenpalm and eigenfinger features

This paper presents a multimodal biometric identification system based on the features of the human hand. We describe a new biometric approach to personal identification using eigenfinger and eigenpalm features, with fusion applied at the matching-score level. The identification process can be divided into the following phases: capturing the image; preprocessing; extracting and normalizing the palm and strip-like finger subimages; extracting the eigenpalm and eigenfinger features based on the K-L transform; matching and fusion; and, finally, a decision based on the (k, l)-NN classifier and thresholding. The system was tested on a database of 237 people (1,820 hand images). The experimental results showed the effectiveness of the system in terms of the recognition rate (100 percent), the equal error rate (EER = 0.58 percent), and the total error rate (TER = 0.72 percent).     

Metacarpophalangeal joint patterns based personal identification system

This paper introduces a novel approach for identity authentication system based on metacarpophalangeal joint patterns (MJPs). A discriminative common vector (DCV) based method is utilized for feature selection. In the literature, there is no study using whole MJP for identity authentication, exceptionally a work (Ferrer et al., 2005) using the hand knuckle pattern which is some part of the MJP draws the attention as a similar study. The originality of this approach is that: whole MJP is firstly used as a biometric identifier and DCV method is firstly applied for extracting the feature set of MJP. The developed system performs some basic tasks like image acquisition, image pre-processing, feature extraction, matching, and performance evaluation. The feasibility and effectiveness of this approach is rigorously evaluated using the k-fold cross validation technique on two different databases: a publicly available database and a specially established database. The experimental results indicate that the MJPs are very distinctive biometric identifiers and can be securely used in biometric identification and verification systems, DCV method is successfully employed for obtaining the feature set of MJPs and proposed MJP based authentication approach is very successful according to state of the art techniques with a recognition rate of between 95.33% and 100.00%.     

Palmprint Verification for Controlling Access to Shared Computing Resources

You can build an effective palmprint verification system using a combination of mostly off-the-shelf components and techniques. Access security is an important aspect of pervasive computing systems. It offers the system developer and end users a certain degree of trust in the use of shared computing resources. Biometrics verification offers many advantages over the username-plus-password approach for access control. Users don't have to memorize any codes or passwords, and biometric systems are more reliable because biometric characteristics can't easily be duplicated, lost, or stolen. Researchers have studied such biometric characteristics as faces, fingerprints, irises, voices, and palmprints.Facial appearance and features change with age. Fingerprints can be affected by surface abrasions or otherwise compromised. Capturing iris images is relatively difficult, and iris scans can be intrusive. Voices are susceptible to noise corruption and can be easily copied and manipulated. Palmprints are potentially a good choice for biometric applications because they're invariant with a person, easy to capture, and difficult to duplicate. They offer greater security than fingerprints because palm veins are more complex than finger veins. However, compared to other biometric characteristics, they have perhaps seen less research. This provides a big opportunity for advancing palmprint technology and applications. We've developed an effective prototype palmprint verification system using a combination of mostly off-the-shelf (and therefore tried and tested) components and techniques. Such an approach should make palmprint verification an appealing proposition.     

Hand shape recognition based on coherent distance shape contexts

In this paper, we propose a novel hand shape recognition method named as Coherent Distance Shape Contexts (CDSC), which is based on two classical shape representations, i.e., Shape Contexts (SC) and Inner-distance Shape Contexts (IDSC). CDSC has good ability to capture discriminative features from hand shape and can well deal with the inexact correspondence problem of hand landmark points. Particularly, it can extract features mainly from the contour of fingers. Thus, it is very robust to different hand poses or elastic deformations of finger valleys. In order to verify the effectiveness of CDSC, we create a new hand image database containing 4000 grayscale left hand images of 200 subjects, on which CDSC has achieved the accurate identification rate of 99.60% for identification and the Equal Error Rate of 0.9% for verification, which are comparable with the state-of-the-art hand shape recognition methods.     

Dynamic tongueprint: A novel biometric identifier

Biometrics, which use human physiological or behavioral features for personal identification, currently face the challenge of designing a secure biometric system that will accept only the legitimate presentation of the biometric identifiers without being fooled by the doctored or spoofed measurements that are input into the system. More biometric traits are required for improving the performance of authentication systems. In this paper, we present a new number for the biometrics family, i.e. tongueprint, which uses particularly interesting properties of the human tongue to base a technology for noninvasive biometric assessment. The tongue is a unique organ which can be stuck out of the mouth for inspection, whose appearance is amenable to examination with the aid of a machine vision system. Yet it is otherwise well protected in the mouth and difficult to be forged. Furthermore, the involuntary squirm of the tongue is not only a convincing proof that the subject is alive, but also a feature for recognition. That is to say, the tongue can present both static features and dynamic features for authentication. However, little work has hitherto been done on the tongue as a biometric identifier. In this work, we make use of a database of tongue images obtained over a long period to examine the performance of the tongueprint as a biometric identifier. Our research shows that tongueprint is a promising candidate for biometric identification and worthy of further research.     

Biometric recognition using 3D ear shape

Previous works have shown that the ear is a promising candidate for biometric identification. However, in prior work, the preprocessing of ear images has had manual steps and algorithms have not necessarily handled problems caused by hair and earrings. We present a complete system for ear biometrics, including automated segmentation of the ear in a profile view image and 3D shape matching for recognition. We evaluated this system with the largest experimental study to date in ear biometrics, achieving a rank-one recognition rate of 97.8 percent for an identification scenario and an equal error rate of 1.2 percent for a verification scenario on a database of 415 subjects and 1,386 total probes.     

Contact-free and pose-invariant hand-biometric-based personal identification system using RGB and depth data

Hand-biometric-based personal identification is considered to be an effective method for automatic recognition. However, existing systems require strict constraints during data acquisition, such as costly devices,specified postures, simple background, and stable illumination. In this paper, a contactless personal identification system is proposed based on matching hand geometry features and color features. An inexpensive Kinect sensor is used to acquire depth and color images of the hand. During image acquisition, no pegs or surfaces are used to constrain hand position or posture. We segment the hand from the background through depth images through a process which is insensitive to illumination and background. Then finger orientations and landmark points, like finger tips or finger valleys, are obtained by geodesic hand contour analysis. Geometric features are extracted from depth images and palmprint features from intensity images. In previous systems, hand features like finger length and width are normalized, which results in the loss of the original geometric features. In our system, we transform 2D image points into real world coordinates, so that the geometric features remain invariant to distance and perspective effects. Extensive experiments demonstrate that the proposed hand-biometric-based personal identification system is effective and robust in various practical situations.     

Comparison and combination of ear and face images in appearance-based biometrics

Researchers have suggested that the ear may have advantages over the face for biometric recognition. Our previous experiments with ear and face recognition, using the standard principal component analysis approach, showed lower recognition performance using ear images. We report results of similar experiments on larger data sets that are more rigorously controlled for relative quality of face and ear images. We find that recognition performance is not significantly different between the face and the ear, for example, 70.5 percent versus 71.6 percent, respectively, in one experiment. We also find that multimodal recognition using both the ear and face results in statistically significant improvement over either individual biometric, for example, 90.9 percent in the analogous experiment.     

On the error-reject trade-off in biometric verification systems

We address the problem of performance evaluation in biometric verification systems. By formulating the optimum Bayesian decision criterion for a verification system and by assuming the data distributions to be multinormals, we derive two statistical expressions for calculating theoretically the false acceptance and false rejection rates. Generally, the adoption of a Bayesian parametric model does not allow for obtaining explicit expressions for the calculation of the system errors. As far as biometric verification systems are concerned, some hypotheses can be reasonably adopted, thus allowing simple and affordable expressions to be derived. By using two verification system prototypes. Based on hand shape and human face, respectively, we show our results are well founded