Sparse Supervised Representation-Based Classifier for Uncontrolled and Imbalanced Classification

Shu, Ting; Zhang, Bob; Tang, Yuan Yan

doi:10.1109/tnnls.2018.2884444

Cited by 27 publications

(6 citation statements)

References 39 publications

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Section: Performance Comparison With Other Modelsmentioning

confidence: 99%

“…Shu et al [58] and Kar et al [59] and others [60]- [62] presented different models for face recognition. However, the performance of these models is not satisfactory as shown in Table 2 as in [58], [59], [62]. We can notice that Tables 2 and 3 obviously explain the proposed model achieves better performance than that of some recent state-of-the-art face and ear recognition models.…”

Section: Performance Comparison With Other Modelsmentioning

confidence: 99%

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A Hybrid Model Combining Learning Distance Metric and DAG Support Vector Machine for Multimodal Biometric Recognition

et al. 2021

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Metric learning has significantly improved machine learning applications such as face re-identification and image classification using K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers. However, to the best of our knowledge, it has not been investigated yet, especially for the multimodal biometric recognition problem in immigration, forensic and surveillance applications with uncontrolled ear datasets. Therefore, it is interesting and very attractive to propose a novel framework for multimodal biometric recognition based on Learning Distance Metric (LDM) via kernel SVM. This paper considers metric learning for SVM by investigating a hybrid Learning Distance Metric and Directed Acyclic Graph SVM (LDM-DAGSVM) model for multimodal biometric recognition, where LDM and DAGSVM are two emerging techniques in dealing with classification problems. Different from existing multimodal biometric recognition methods, the proposed approach aims to learn Mahalanobis distance metric via kernel SVM to maximize the inter-class variations and minimize the intra-class variations, simultaneously. Experimental results on the uncontrolled datasets such as AR face and AWE ear datasets show that the proposed approach achieves competitive performance compared with models working on individual modalities and overperforms the state-of-the-art multimodal methods. The proposed model achieves fivefold classification accuracy around 99.85 % for the face and ear images. INDEX TERMSBiometrics, multimodal biometrics, face and ear images, Mahalanobis distance, metric learning, DAGSVM. AHMED HAGAG (Member, IEEE) received the B.Sc. degree (Hons.

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Section: Performance Comparison With Other Modelsmentioning

confidence: 99%

Section: Performance Comparison With Other Modelsmentioning

confidence: 99%

A Hybrid Model Combining Learning Distance Metric and DAG Support Vector Machine for Multimodal Biometric Recognition

et al. 2021

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“…Visual applications like remote sensing [2], face recognition [3], [4], object recognition [5], biometrics [6]- [8] widely use the models and algorithms of image classification. The linear representation-based classification method is an active research area in the image classification field [9]- [11]. Up to now, extensive LRC methods have been proposed and developed for better and more robust image classification, such as Sparse representation-based classifier (SRC) [9], Collaborative representation-based classifier (CRC) [10], Non-negative representation-based classifier (NRC) [12], and so on.…”

Section: Introductionmentioning

confidence: 99%

Linear Representation-Based Methods for Image Classification: A Survey

2020

Self Cite

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In recent years, linear representation-based methods have been widely researched and applied in the image classification field. Generally speaking, there are three steps within linear representation-based classification (LRC) algorithms. The first step is coding, which uses all training samples to represent the test sample in a linear combination. The second step is subspace approximation, where residuals between the test sample and the linear combination of each class are calculated. The third step is classification, which assigns the class label to the minimum class-specific residual. We classify the LRC methods into six categories: 1) linear representation-based classification methods with norm minimizations, 2) linear representationbased classification methods with constraints, 3) linear representation-based classification methods with feature spaces, 4) linear representation-based classification methods with structural information, 5) linear representation with subspace learning, and 6) linear representation in semi-supervised learning and unsupervised learning. The purpose of this paper is to: 1) make an accurate and clear definition of the linear representation-based method, 2) provide a categorization and a comprehensive survey of the existing linear representation-based classification methods for image classification, 3) Summarize the main applications of linear representation-based methods, 4) provide extensive classification results and a discussion of the linear representation-based methods. Furthermore, this paper summarizes specific applications of the linear representation-based methods. Particularly, we performed extensive experiments to compare twelve linear representation-based classification methods on seven image classification datasets.

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“…SRC could not obtain satisfactory results on uncontrolled or imbalanced data sets. Sparse supervised representation classifier (SSRC) was proposed to solve these issues [7].…”

Section: Introductionmentioning

confidence: 99%

Improvements on Learning Kernel Extended Dictionary for Face Recognition

Amanzadeh¹,

Forghani²,

Chabok³

2020

RIA

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Kernel extended dictionary learning model (KED) is a new type of Sparse Representation for Classification (SRC), which represents the input face image as a linear combination of dictionary set and extended dictionary set to determine the input face image class label. Extended dictionary is created based on the differences between the occluded images and non-occluded training images. There are four defaults to make about KED: (1) Similar weights are assigned to the principle components of occlusion variations in KED model, while the principle components of the occlusion variations have different weights, which are proportional to the principle components Eigen-values. (2) Reconstruction of an occluded image is not possible by combining only non-occluded images and the principle components (or the directions) of occlusion variations, but it requires the mean of occlusion variations. (3) The importance and capability of main dictionary and extended dictionary in reconstructing the input face image is not the same, necessarily. (4) KED Runtime is high. To address these problems or challenges, a novel mathematical model is proposed in this paper. In the proposed model, different weights are assigned to the principle components of occlusion variations; different weights are assigned to the main dictionary and extended dictionary; an occluded image is reconstructed by non-occluded images and the principle components of occlusion variations, and also the mean of occlusion variations; and collaborative representation is used instead of sparse representation to enhance the runtime. Experimental results on CAS-PEAL subsets showed that the runtime and accuracy of the proposed model is about 1% better than that of KED.

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Sparse Supervised Representation-Based Classifier for Uncontrolled and Imbalanced Classification

Cited by 27 publications

References 39 publications

A Hybrid Model Combining Learning Distance Metric and DAG Support Vector Machine for Multimodal Biometric Recognition

A Hybrid Model Combining Learning Distance Metric and DAG Support Vector Machine for Multimodal Biometric Recognition

Linear Representation-Based Methods for Image Classification: A Survey

Improvements on Learning Kernel Extended Dictionary for Face Recognition

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