The Experts below are selected from a list of 194013 Experts worldwide ranked by ideXlab platform
Pong C Yuen - One of the best experts on this subject based on the ideXlab platform.
-
very low resolution face Recognition Problem
IEEE Transactions on Image Processing, 2012Co-Authors: W W Zou, Pong C YuenAbstract:This paper addresses the very low resolution (VLR) Problem in face Recognition in which the resolution of the face image to be recognized is lower than 16 × 16. With the increasing demand of surveillance camera-based applications, the VLR Problem happens in many face application systems. Existing face Recognition algorithms are not able to give satisfactory performance on the VLR face image. While face super-resolution (SR) methods can be employed to enhance the resolution of the images, the existing learning-based face SR methods do not perform well on such a VLR face image. To overcome this Problem, this paper proposes a novel approach to learn the relationship between the high-resolution image space and the VLR image space for face SR. Based on this new approach, two constraints, namely, new data and discriminative constraints, are designed for good visuality and face Recognition applications under the VLR Problem, respectively. Experimental results show that the proposed SR algorithm based on relationship learning outperforms the existing algorithms in public face databases.
-
very low resolution face Recognition Problem
International Conference on Biometrics: Theory Applications and Systems, 2010Co-Authors: W Zou W Wilman, Pong C YuenAbstract:This paper addresses the very low resolution (VLR) Problem in face Recognition in which the resolution of face image to be recognized is lower than 16×16. The VLR Problem happens in many surveillance camera-based applications and existing face Recognition algorithms are not able to give satisfactory performance on VLR face image. While face super-resolution (SR) methods can be employed to enhance the resolution of the images, the existing learning-based face SR methods do not perform well on such a very low resolution face image. To overcome this Problem, this paper models the SR Problem under VLR case as a regression Problem with two constraints. First, a new data constraint is design to perform the error measurement on high resolution image space which provides more detailed and discriminative information. Second, discriminative constraint is proposed and incorporated in the training stage so that the reconstructed HR image has higher discriminability. CMU-PIE, FRGC and surveillant camera face (SCface) databases are selected for experiments. Experimental results show that the proposed method outperforms the existing methods, in terms of image quality and Recognition accuracy.
Jingyu Yang - One of the best experts on this subject based on the ideXlab platform.
-
face and palmprint pixel level fusion and kernel dcv rbf classifier for small sample biometric Recognition
Pattern Recognition, 2007Co-Authors: Xiaoyuan Jing, David Zhang, Jingyu Yang, Miao LiAbstract:Recently, multi-modal biometric fusion techniques have attracted increasing atove the Recognition performance in some difficult biometric Problems. The small sample biometric Recognition Problem is such a research difficulty in real-world applications. So far, most research work on fusion techniques has been done at the highest fusion level, i.e. the decision level. In this paper, we propose a novel fusion approach at the lowest level, i.e. the image pixel level. We first combine two kinds of biometrics: the face feature, which is a representative of contactless biometric, and the palmprint feature, which is a typical contacting biometric. We perform the Gabor transform on face and palmprint images and combine them at the pixel level. The correlation analysis shows that there is very small correlation between their normalized Gabor-transformed images. This paper also presents a novel classifier, KDCV-RBF, to classify the fused biometric images. It extracts the image discriminative features using a Kernel discriminative common vectors (KDCV) approach and classifies the features by using the radial base function (RBF) network. As the test data, we take two largest public face databases (AR and FERET) and a large palmprint database. The experimental results demonstrate that the proposed biometric fusion Recognition approach is a rather effective solution for the small sample Recognition Problem.
-
face and palmprint pixel level fusion and kernel dcv rbf classifier for small sample biometric Recognition
Pattern Recognition, 2007Co-Authors: Xiaoyuan Jing, David Zhang, Yongfang Yao, Jingyu YangAbstract:Recently, multi-modal biometric fusion techniques have attracted increasing atove the Recognition performance in some difficult biometric Problems. The small sample biometric Recognition Problem is such a research difficulty in real-world applications. So far, most research work on fusion techniques has been done at the highest fusion level, i.e. the decision level. In this paper, we propose a novel fusion approach at the lowest level, i.e. the image pixel level. We first combine two kinds of biometrics: the face feature, which is a representative of contactless biometric, and the palmprint feature, which is a typical contacting biometric. We perform the Gabor transform on face and palmprint images and combine them at the pixel level. The correlation analysis shows that there is very small correlation between their normalized Gabor-transformed images. This paper also presents a novel classifier, KDCV-RBF, to classify the fused biometric images. It extracts the image discriminative features using a Kernel discriminative common vectors (KDCV) approach and classifies the features by using the radial base function (RBF) network. As the test data, we take two largest public face databases (AR and FERET) and a large palmprint database. The experimental results demonstrate that the proposed biometric fusion Recognition approach is a rather effective solution for the small sample Recognition Problem.
Xiaoyuan Jing - One of the best experts on this subject based on the ideXlab platform.
-
face and palmprint pixel level fusion and kernel dcv rbf classifier for small sample biometric Recognition
Pattern Recognition, 2007Co-Authors: Xiaoyuan Jing, David Zhang, Jingyu Yang, Miao LiAbstract:Recently, multi-modal biometric fusion techniques have attracted increasing atove the Recognition performance in some difficult biometric Problems. The small sample biometric Recognition Problem is such a research difficulty in real-world applications. So far, most research work on fusion techniques has been done at the highest fusion level, i.e. the decision level. In this paper, we propose a novel fusion approach at the lowest level, i.e. the image pixel level. We first combine two kinds of biometrics: the face feature, which is a representative of contactless biometric, and the palmprint feature, which is a typical contacting biometric. We perform the Gabor transform on face and palmprint images and combine them at the pixel level. The correlation analysis shows that there is very small correlation between their normalized Gabor-transformed images. This paper also presents a novel classifier, KDCV-RBF, to classify the fused biometric images. It extracts the image discriminative features using a Kernel discriminative common vectors (KDCV) approach and classifies the features by using the radial base function (RBF) network. As the test data, we take two largest public face databases (AR and FERET) and a large palmprint database. The experimental results demonstrate that the proposed biometric fusion Recognition approach is a rather effective solution for the small sample Recognition Problem.
-
face and palmprint pixel level fusion and kernel dcv rbf classifier for small sample biometric Recognition
Pattern Recognition, 2007Co-Authors: Xiaoyuan Jing, David Zhang, Yongfang Yao, Jingyu YangAbstract:Recently, multi-modal biometric fusion techniques have attracted increasing atove the Recognition performance in some difficult biometric Problems. The small sample biometric Recognition Problem is such a research difficulty in real-world applications. So far, most research work on fusion techniques has been done at the highest fusion level, i.e. the decision level. In this paper, we propose a novel fusion approach at the lowest level, i.e. the image pixel level. We first combine two kinds of biometrics: the face feature, which is a representative of contactless biometric, and the palmprint feature, which is a typical contacting biometric. We perform the Gabor transform on face and palmprint images and combine them at the pixel level. The correlation analysis shows that there is very small correlation between their normalized Gabor-transformed images. This paper also presents a novel classifier, KDCV-RBF, to classify the fused biometric images. It extracts the image discriminative features using a Kernel discriminative common vectors (KDCV) approach and classifies the features by using the radial base function (RBF) network. As the test data, we take two largest public face databases (AR and FERET) and a large palmprint database. The experimental results demonstrate that the proposed biometric fusion Recognition approach is a rather effective solution for the small sample Recognition Problem.
Miao Li - One of the best experts on this subject based on the ideXlab platform.
-
face and palmprint pixel level fusion and kernel dcv rbf classifier for small sample biometric Recognition
Pattern Recognition, 2007Co-Authors: Xiaoyuan Jing, David Zhang, Jingyu Yang, Miao LiAbstract:Recently, multi-modal biometric fusion techniques have attracted increasing atove the Recognition performance in some difficult biometric Problems. The small sample biometric Recognition Problem is such a research difficulty in real-world applications. So far, most research work on fusion techniques has been done at the highest fusion level, i.e. the decision level. In this paper, we propose a novel fusion approach at the lowest level, i.e. the image pixel level. We first combine two kinds of biometrics: the face feature, which is a representative of contactless biometric, and the palmprint feature, which is a typical contacting biometric. We perform the Gabor transform on face and palmprint images and combine them at the pixel level. The correlation analysis shows that there is very small correlation between their normalized Gabor-transformed images. This paper also presents a novel classifier, KDCV-RBF, to classify the fused biometric images. It extracts the image discriminative features using a Kernel discriminative common vectors (KDCV) approach and classifies the features by using the radial base function (RBF) network. As the test data, we take two largest public face databases (AR and FERET) and a large palmprint database. The experimental results demonstrate that the proposed biometric fusion Recognition approach is a rather effective solution for the small sample Recognition Problem.
David Zhang - One of the best experts on this subject based on the ideXlab platform.
-
face and palmprint pixel level fusion and kernel dcv rbf classifier for small sample biometric Recognition
Pattern Recognition, 2007Co-Authors: Xiaoyuan Jing, David Zhang, Jingyu Yang, Miao LiAbstract:Recently, multi-modal biometric fusion techniques have attracted increasing atove the Recognition performance in some difficult biometric Problems. The small sample biometric Recognition Problem is such a research difficulty in real-world applications. So far, most research work on fusion techniques has been done at the highest fusion level, i.e. the decision level. In this paper, we propose a novel fusion approach at the lowest level, i.e. the image pixel level. We first combine two kinds of biometrics: the face feature, which is a representative of contactless biometric, and the palmprint feature, which is a typical contacting biometric. We perform the Gabor transform on face and palmprint images and combine them at the pixel level. The correlation analysis shows that there is very small correlation between their normalized Gabor-transformed images. This paper also presents a novel classifier, KDCV-RBF, to classify the fused biometric images. It extracts the image discriminative features using a Kernel discriminative common vectors (KDCV) approach and classifies the features by using the radial base function (RBF) network. As the test data, we take two largest public face databases (AR and FERET) and a large palmprint database. The experimental results demonstrate that the proposed biometric fusion Recognition approach is a rather effective solution for the small sample Recognition Problem.
-
face and palmprint pixel level fusion and kernel dcv rbf classifier for small sample biometric Recognition
Pattern Recognition, 2007Co-Authors: Xiaoyuan Jing, David Zhang, Yongfang Yao, Jingyu YangAbstract:Recently, multi-modal biometric fusion techniques have attracted increasing atove the Recognition performance in some difficult biometric Problems. The small sample biometric Recognition Problem is such a research difficulty in real-world applications. So far, most research work on fusion techniques has been done at the highest fusion level, i.e. the decision level. In this paper, we propose a novel fusion approach at the lowest level, i.e. the image pixel level. We first combine two kinds of biometrics: the face feature, which is a representative of contactless biometric, and the palmprint feature, which is a typical contacting biometric. We perform the Gabor transform on face and palmprint images and combine them at the pixel level. The correlation analysis shows that there is very small correlation between their normalized Gabor-transformed images. This paper also presents a novel classifier, KDCV-RBF, to classify the fused biometric images. It extracts the image discriminative features using a Kernel discriminative common vectors (KDCV) approach and classifies the features by using the radial base function (RBF) network. As the test data, we take two largest public face databases (AR and FERET) and a large palmprint database. The experimental results demonstrate that the proposed biometric fusion Recognition approach is a rather effective solution for the small sample Recognition Problem.
