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179
An introduction to biometric recognition
- IEEE Transactions on Circuits and Systems for Video Technology
, 2004
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Information fusion in biometrics
- Pattern Recognition Letters
, 2003
"... User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom, non-universality of the biometric trait and unacceptable error rates. Attempting to improve the performance of individual matchers in such situations may not p ..."
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Cited by 282 (17 self)
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User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom, non-universality of the biometric trait and unacceptable error rates. Attempting to improve the performance of individual matchers in such situations may not prove to be effective because of these inherent problems. Multibiometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the same identity. These systems help achieve an increase in performance that may not be possible using a single biometric indicator. Further, multibiometric systems provide anti-spoofing measures by making it difficult for an intruder to spoof multiple biometric traits simultaneously. However, an effective fusion scheme is necessary to combine the information presented by multiple domain experts. This paper addresses the problem of information fusion in biometric verification systems by combining information at the matching score level. Experimental results on combining three biometric modalities (face, fingerprint and hand geometry) are presented.
Biometrics: A tool for information security
- IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
, 2006
"... Establishing identity is becoming critical in our vastly interconnected society. Questions such as “Is she really who she claims to be?, ” “Is this person authorized to use this facility?, ” or “Is he in the watchlist posted by the government? ” are routinely being posed in a variety of scenarios r ..."
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Cited by 173 (4 self)
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Establishing identity is becoming critical in our vastly interconnected society. Questions such as “Is she really who she claims to be?, ” “Is this person authorized to use this facility?, ” or “Is he in the watchlist posted by the government? ” are routinely being posed in a variety of scenarios ranging from issuing a driver’s license to gaining entry into a country. The need for reliable user authentication techniques has increased in the wake of heightened concerns about security and rapid advancements in networking, communication, and mobility. Biometrics, described as the science of recognizing an individual based on his or her physical or behavioral traits, is beginning to gain acceptance as a legitimate method for determining an individual’s identity. Biometric systems have now been deployed in various commercial, civilian, and forensic applications as a means of establishing identity. In this paper, we provide an overview of biometrics and discuss some of the salient research issues that need to be addressed for making biometric technology an effective tool for providing information security. The primary contribution of this overview includes: 1) examining applications where biometrics can solve issues pertaining to information security; 2) enumerating the fundamental challenges encountered by biometric systems in real-world applications; and 3) discussing solutions to address the problems of scalability and security in large-scale authentication systems.
Fusion of face and speech data for person identity verification
- IEEE Trans. Neural Networks
, 1999
"... Abstract—Biometric person identity authentication is gaining more and more attention. The authentication task performed by an expert is a binary classification problem: reject or accept identity claim. Combining experts, each based on a different modality (speech, face, fingerprint, etc.), increases ..."
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Cited by 120 (0 self)
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Abstract—Biometric person identity authentication is gaining more and more attention. The authentication task performed by an expert is a binary classification problem: reject or accept identity claim. Combining experts, each based on a different modality (speech, face, fingerprint, etc.), increases the performance and robustness of identity authentication systems. In this context, a key issue is the fusion of the different experts for taking a final decision (i.e., accept or reject identity claim). We propose to evaluate different binary classification schemes (support vector machine, multilayer perceptron, C4.5 decision tree, Fisher’s linear discriminant, Bayesian classifier) to carry on the fusion. The experimental results show that support vector machines and Bayesian classifier achieve almost the same performances, and both outperform the other evaluated classifiers. Index Terms—Bayesian decision, binary classifiers, biometrics, data fusion, face recognition, speaker recognition, support vector machine. I.
Personal Verification using Palmprint and Hand Geometry Biometric
, 2003
"... A new approach for the personal identification using hand images is presented. This paper attempts to improve the performance of palmprint-based verification system by integra t ing hand geometry features. Unlike other bimodal biometric systems, the users do not have to undergo the inconvenience ..."
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Cited by 104 (10 self)
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A new approach for the personal identification using hand images is presented. This paper attempts to improve the performance of palmprint-based verification system by integra t ing hand geometry features. Unlike other bimodal biometric systems, the users do not have to undergo the inconvenience of usingtwo different sensors since the palmprint and hand geometry features can be acquired from the same image, using a digital camera, at the same time. Each of these gray level images are aligned and then used to extract palmprint and hand geometry features. These features are then examined for their individual and combined performances. The image acquisition setup used in this work is inherently simple and it does not employ any special illumination nor does it use any pegs to cause any inconvenience to the users. Our experimental results on the image dataset from 100 users confirm the utility of combining hand geometry features with those from palmprints usinga simple image acquis ition setup.
Recent advances in visual and infrared face recognition - a review
- Computer Vision and Image Understanding
, 2005
"... Face recognition is a rapidly growing research area due to increasing demands for security in commercial and law enforcement applications. This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional (2D) images in the visual and infrared (IR) ..."
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Cited by 101 (9 self)
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Face recognition is a rapidly growing research area due to increasing demands for security in commercial and law enforcement applications. This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional (2D) images in the visual and infrared (IR) spectra. Face recognition systems based on visual images have reached a significant level of maturity with some practical success. However, the performance of visual face recognition may degrade under poor illumination conditions or for subjects of various skin colors. IR imagery represents a viable alternative to visible imaging in the search for a robust and practical identification system. While visual face recognition systems perform relatively reliably under controlled illumination conditions, thermal IR face recognition systems are advantageous when there is no control over illumination or for detecting disguised faces. Face recognition using 3D images is another active area of face recognition, which provides robust face recognition with changes in pose. Recent research has also demonstrated that the fusion of different imaging modalities and spectral components can improve the overall performance of face recognition.
Face recognition using 2D and 3D facial data
- ACM Workshop on Multimodal User Authentication
, 2003
"... Results are presented for the largest experimental study to date that investigates the comparison and combination of 2D and 3D face recognition. To our knowledge, this is also the only such study to incorporate significant time lapse between gallery and probe image acquisition, and to look at the ef ..."
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Cited by 93 (13 self)
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Results are presented for the largest experimental study to date that investigates the comparison and combination of 2D and 3D face recognition. To our knowledge, this is also the only such study to incorporate significant time lapse between gallery and probe image acquisition, and to look at the effect of depth resolution. Recognition results are obtained in (1) single gallery and a single probe study, and (2) a single gallery and multiple probe study. A total of 275 subjects participated in one or more data acquisition sessions. Results are presented for gallery and probe datasets of 200 subjects imaged in both 2D and 3D, with one to thirteen weeks time lapse between gallery and probe images of a given subject yielding 951 pairs of 2D and 3D images. Using a PCA-based approach tuned separately for 2D and for 3D, we find that 3D outperforms 2D. However, we also find a multi-modal rank-one recognition rate of 98.5 % in a single probe study and 98.8 % in multi-probe study, which is statistically significantly greater than either 2D or 3D alone. 1.
Multimodal biometrics: an overview
- Proc. of 12th European Signal Processing Conference (EUSIPCO
, 2004
"... Unimodal biometric systems have to contend with a vari-ety of problems such as noisy data, intra-class variations, re-stricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates. Some of these limitations can be addressed by deploying multimodal biometric systems that ..."
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Cited by 90 (0 self)
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Unimodal biometric systems have to contend with a vari-ety of problems such as noisy data, intra-class variations, re-stricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates. Some of these limitations can be addressed by deploying multimodal biometric systems that integrate the evidence presented by multiple sources of in-formation. This paper discusses the various scenarios that are possible in multimodal biometric systems, the levels of fusion that are plausible and the integration strategies that can be adopted to consolidate information. We also present several examples of multimodal systems that have been de-scribed in the literature. 1.
Comparison and combination of ear and face images in appearance-based biometrics
- IEEE Trans. Pattern Analysis and Machine Intelligence
, 2003
"... Abstract—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 ..."
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Cited by 85 (15 self)
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Abstract—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. Index Terms—Biometrics, multimodal biometrics, face recognition, ear recognition, appearance-based recognition, principal component analysis. 1
Multi-Modal 2D and 3D Biometrics for Face Recognition
- in Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG03
"... Results are presented for the largest experimental study to date that investigates the comparison and combination of 2D and 3D face data for biometric recognition. To our knowledge, this is also the only such study to incorporate significant time lapse between gallery and probe image ac-quisition. R ..."
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Cited by 55 (8 self)
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Results are presented for the largest experimental study to date that investigates the comparison and combination of 2D and 3D face data for biometric recognition. To our knowledge, this is also the only such study to incorporate significant time lapse between gallery and probe image ac-quisition. Recognition results are presented for gallery and probe datasets of 166 subjects imaged in both 2D and 3D, with six to thirteen weeks time lapse between gallery and probe images of a given subject. Using a PCA-based ap-proach tuned separately for 2D and for 3D, we find no sta-tistically significant difference between the rank-one recog-nition rates of 83.1 % for 2D and 83.7 % for 3D. Using a certainty-weighted sum-of-distance approach to combining 2D and 3D, we find a multi-modal rank-one recognition rate of 92.8%, which is statistically significantly greater than ei-ther 2D or 3D alone. 1.
