Local discriminative distance metrics ensemble learning

Puthenputhussery

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

2016

Anthropology studies discover that some genetic related facial features, which are inherited by children from their parents, can be used for kinship verification. This paper investigates an important inheritable feature -color and presents a novel inheritable color space (InCS) and a generalized InCS (GInCS) framework with application to kinship verification. Specifically, a novel color similarity measure (CSM) is first defined. Second, based on this similarity measure, a new inheritable color space (InCS) is derived by balancing the criterion of minimizing the distance between kinship pairs and the criterion of maximizing the distance between non-kinship pairs. Unlike conventional color spaces, e.g. the RGB color space, the proposed InCS, which is learned automatically from the data, captures the inheritable information between parent and child. Third, theoretical and empirical analysis show that the proposed InCS exhibits the decorrelation property, which is positively related to the performance of kinship verification. Robustness to the illumination variation is also discussed. Fourth, a generalized InCS framework is presented to extend the InCS from the pixel level to the feature level for improving the performance and the robustness to illumination variation. The proposed InCS is evaluated on several popular datasets, namely the KinFaceW-I dataset, the KinFaceW-II dataset, the UB KinFace dataset, and the Cornell KinFace dataset. Experimental results show that the proposed InCS is able to (i) improve the conventional color spaces such as RGB, YUV, YIQ color spaces by a large margin, (ii) achieve robustness to the illumination variation, and (iii) outperforms other popular methods.

Section: Methodsmentioning

confidence: 99%

A novel inheritable color space with application to kinship verification

Puthenputhussery

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

2016

“…The existing Mahalanobis metric learning can be classified into two categories [21], global-based and local-based. Global-based methods try to make the samples of same class close and the samples of different class apart by using only pairwise distance constraints.…”

Section: ) Mahalanobis Metric Learningmentioning

confidence: 99%

Heterogeneous Face Recognition by Margin-Based Cross-Modality Metric Learning

Huo

Shi

Yang

et al. 2018

IEEE Trans. Cybern.

Abstract-Heterogeneous face recognition deals with matching face images from different modalities or sources. The main challenge lies in cross-modal differences and variations and the goal is to make cross-modality separation among subjects. A margin-based cross-modality metric learning (MCM 2 L) method is proposed to address the problem. A cross-modality metric is defined in a common subspace where samples of two different modalities are mapped and measured. The objective is to learn such metrics that satisfy the following two constraints. The first minimizes pairwise, intrapersonal cross-modality distances. The second forces a margin between subject specific intrapersonal and interpersonal cross-modality distances. This is achieved by defining a hinge loss on triplet-based distance constraints for efficient optimization. It allows the proposed method to focus more on optimizing distances of those subjects whose intrapersonal and interpersonal distances are hard to separate. The proposed method is further extended to a kernelized MCM 2 L (KMCM 2 L). Both methods have been evaluated on an ID card face dataset and two other cross-modality benchmark datasets. Various feature extraction methods have also been incorporated in the study, including recent deep learned features. In extensive experiments and comparisons with the state-of-the-art methods, the MCM 2 L and KMCM 2 L methods achieved marked improvements in most cases.

“…Although they have been successfully applied to various applications ranging from unsupervised segmentation of switching dynamics to face recognition or image fusion [12][13][14], a drawback of this kind of classifier is that their solutions does not take into consideration of the local manifold structure and the potential discriminant information hidden in the data. In order to overcome this drawback of SVM and t-SVM, a modified version of SVM called Laplacian Support Vector Machine (Lap-SVM) [15] based on Manifold Regularization (MR) framework was proposed by combining the locality preserving projections (LPP) [16,17] and SVM. LPP not only can preserve the local manifold structure of the dataset, but also can obtain a linear projection on new test samples for dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

υ-Support vector machine based on discriminant sparse neighborhood preserving embedding

Fang

Huang

et al. 2016

Pattern Anal Applic

In this paper, we mainly focus on two issues (1) SVM is very sensitive to noise. (2) The solution of SVM does not take into consideration of the intrinsic structure and the discriminant information of the data. To address these two problems, we first propose an integration model to integrate both the local manifold structure and the local discriminant information into ' 1 graph embedding. Then we add the integration model into the objection function of t-support vector machine. Therefore, a discriminant sparse neighborhood preserving embedding t-support vector machine (t-DSNPESVM) method is proposed. The theoretical analysis demonstrates that t-DSNPESVM is a reasonable maximum margin classifier and can obtain a very lower generalization error upper bound by minimizing the integration model and the upper bound of margin error. Moreover, in the nonlinear case, we construct the kernel sparse representation-based ' 1 graph for t-DSNPESVM, which is more conducive to improve the classification accuracy than ' 1 graph constructed in the original space.Experimental results on real datasets show the effectiveness of the proposed t-DSNPESVM method.