The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Anil K Jain - One of the best experts on this subject based on the ideXlab platform.
-
score normalization in Multimodal biometric Systems
Pattern Recognition, 2005Co-Authors: Anil K Jain, Karthik Nandakumar, Arun RossAbstract:Multimodal biometric Systems consolidate the evidence presented by multiple biometric sources and typically provide better recognition performance compared to Systems based on a single biometric modality. Although information fusion in a Multimodal System can be performed at various levels, integration at the matching score level is the most common approach due to the ease in accessing and combining the scores generated by different matchers. Since the matching scores output by the various modalities are heterogeneous, score normalization is needed to transform these scores into a common domain, prior to combining them. In this paper, we have studied the performance of different normalization techniques and fusion rules in the context of a Multimodal biometric System based on the face, fingerprint and hand-geometry traits of a user. Experiments conducted on a database of 100 users indicate that the application of min-max, z-score, and tanh normalization schemes followed by a simple sum of scores fusion method results in better recognition performance compared to other methods. However, experiments also reveal that the min-max and z-score normalization techniques are sensitive to outliers in the data, highlighting the need for a robust and efficient normalization procedure like the tanh normalization. It was also observed that Multimodal Systems utilizing user-specific weights perform better compared to Systems that assign the same set of weights to the multiple biometric traits of all users.
-
integrating faces fingerprints and soft biometric traits for user recognition
Lecture Notes in Computer Science, 2004Co-Authors: Anil K Jain, Karthik Nandakumar, Xiaoguang Lu, Unsang ParkAbstract:Soft biometric traits like gender, age, height, weight, ethnicity, and eye color cannot provide reliable user recognition because they are not distinctive and permanent. However, such ancillary information can complement the identity information provided by the primary biometric traits (face, fingerprint, hand-geometry, iris, etc.). This paper describes a hybrid biometric System that uses face and fingerprint as the primary characteristics and gender, ethnicity, and height as the soft characteristics. We have studied the effect of the soft biometric traits on the recognition performance of unimodal face and fingerprint recognition Systems and a Multimodal System that uses both the primary traits. Experiments conducted on a database of 263 users show that the recognition performance of the primary biometric System can be improved significantly by making use of soft biometric information. The results also indicate that such a performance improvement can be achieved only if the soft biometric traits are complementary to the primary biometric traits.
Unsang Park - One of the best experts on this subject based on the ideXlab platform.
-
integrating faces fingerprints and soft biometric traits for user recognition
Lecture Notes in Computer Science, 2004Co-Authors: Anil K Jain, Karthik Nandakumar, Xiaoguang Lu, Unsang ParkAbstract:Soft biometric traits like gender, age, height, weight, ethnicity, and eye color cannot provide reliable user recognition because they are not distinctive and permanent. However, such ancillary information can complement the identity information provided by the primary biometric traits (face, fingerprint, hand-geometry, iris, etc.). This paper describes a hybrid biometric System that uses face and fingerprint as the primary characteristics and gender, ethnicity, and height as the soft characteristics. We have studied the effect of the soft biometric traits on the recognition performance of unimodal face and fingerprint recognition Systems and a Multimodal System that uses both the primary traits. Experiments conducted on a database of 263 users show that the recognition performance of the primary biometric System can be improved significantly by making use of soft biometric information. The results also indicate that such a performance improvement can be achieved only if the soft biometric traits are complementary to the primary biometric traits.
Karthik Nandakumar - One of the best experts on this subject based on the ideXlab platform.
-
score normalization in Multimodal biometric Systems
Pattern Recognition, 2005Co-Authors: Anil K Jain, Karthik Nandakumar, Arun RossAbstract:Multimodal biometric Systems consolidate the evidence presented by multiple biometric sources and typically provide better recognition performance compared to Systems based on a single biometric modality. Although information fusion in a Multimodal System can be performed at various levels, integration at the matching score level is the most common approach due to the ease in accessing and combining the scores generated by different matchers. Since the matching scores output by the various modalities are heterogeneous, score normalization is needed to transform these scores into a common domain, prior to combining them. In this paper, we have studied the performance of different normalization techniques and fusion rules in the context of a Multimodal biometric System based on the face, fingerprint and hand-geometry traits of a user. Experiments conducted on a database of 100 users indicate that the application of min-max, z-score, and tanh normalization schemes followed by a simple sum of scores fusion method results in better recognition performance compared to other methods. However, experiments also reveal that the min-max and z-score normalization techniques are sensitive to outliers in the data, highlighting the need for a robust and efficient normalization procedure like the tanh normalization. It was also observed that Multimodal Systems utilizing user-specific weights perform better compared to Systems that assign the same set of weights to the multiple biometric traits of all users.
-
integrating faces fingerprints and soft biometric traits for user recognition
Lecture Notes in Computer Science, 2004Co-Authors: Anil K Jain, Karthik Nandakumar, Xiaoguang Lu, Unsang ParkAbstract:Soft biometric traits like gender, age, height, weight, ethnicity, and eye color cannot provide reliable user recognition because they are not distinctive and permanent. However, such ancillary information can complement the identity information provided by the primary biometric traits (face, fingerprint, hand-geometry, iris, etc.). This paper describes a hybrid biometric System that uses face and fingerprint as the primary characteristics and gender, ethnicity, and height as the soft characteristics. We have studied the effect of the soft biometric traits on the recognition performance of unimodal face and fingerprint recognition Systems and a Multimodal System that uses both the primary traits. Experiments conducted on a database of 263 users show that the recognition performance of the primary biometric System can be improved significantly by making use of soft biometric information. The results also indicate that such a performance improvement can be achieved only if the soft biometric traits are complementary to the primary biometric traits.
Xiaoguang Lu - One of the best experts on this subject based on the ideXlab platform.
-
integrating faces fingerprints and soft biometric traits for user recognition
Lecture Notes in Computer Science, 2004Co-Authors: Anil K Jain, Karthik Nandakumar, Xiaoguang Lu, Unsang ParkAbstract:Soft biometric traits like gender, age, height, weight, ethnicity, and eye color cannot provide reliable user recognition because they are not distinctive and permanent. However, such ancillary information can complement the identity information provided by the primary biometric traits (face, fingerprint, hand-geometry, iris, etc.). This paper describes a hybrid biometric System that uses face and fingerprint as the primary characteristics and gender, ethnicity, and height as the soft characteristics. We have studied the effect of the soft biometric traits on the recognition performance of unimodal face and fingerprint recognition Systems and a Multimodal System that uses both the primary traits. Experiments conducted on a database of 263 users show that the recognition performance of the primary biometric System can be improved significantly by making use of soft biometric information. The results also indicate that such a performance improvement can be achieved only if the soft biometric traits are complementary to the primary biometric traits.
Marc Van Droogenbroeck - One of the best experts on this subject based on the ideXlab platform.
-
frontal view gait recognition by intra and inter frame rectangle size distribution
Pattern Recognition Letters, 2009Co-Authors: Olivier Barnich, Marc Van DroogenbroeckAbstract:Current trends seem to accredit gait as a sensible biometric feature for human identification, at least in a Multimodal System. In addition to being a robust feature, gait is hard to fake and requires no cooperation from the user. As in many video Systems, the recognition confidence relies on the angle of view of the camera and on the illumination conditions, inducing a sensitivity to operational conditions that one may wish to lower. In this paper we present an efficient approach capable of recognizing people in frontal-view video sequences. The approach uses an intra-frame description of silhouettes which consists of a set of rectangles that will fit into any closed silhouette. A dynamic, inter-frame, dimension is then added by aggregating the size distributions of these rectangles over multiple successive frames. For each new frame, the inter-frame gait signature is updated and used to estimate the identity of the person detected in the scene. Finally, in order to smooth the decision on the identity, a majority vote is applied to previous results. In the final part of this article, we provide experimental results and discuss the accuracy of the classification for our own database of 21 known persons, and for a public database of 25 persons.