Meng Pang

Meng Pang
Nanchang University

PhD

About

21
Publications
2,264
Reads
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148
Citations
Introduction
Meng Pang received the Ph.D. degree from the Department of Computer Science, Hong Kong Baptist University, Hong Kong, China, in 2019, and received the B.Sc. and M.Sc. degrees in software engineering from Dalian University of Technology, Dalian, China, in 2013 and 2016, respectively. He is currently a Research Fellow with the School of Electrical and Electronic Engineering, Nanyang Technological University. His research interests include image processing and adversarial machine learning.

Publications

Publications (21)
Article
The key ingredients of matrix factorization lie in the basis learning and coefficient representation. To enhance the discriminant ability of the learnt basis, discriminant graph embedding is usually introduced in matrix factorization model. However, existing matrix factorization methods based on graph embedding generally conduct discriminant analys...
Article
Single sample per person face recognition (SSPP FR), i.e. identifying a person (i.e., data subject) with a single face image only for training, has several attractive potential applications, but is still a challenging problem. Existing generic learning methods usually leverage prototype plus variation (P+V) model for SSPP FR provided that face samp...
Article
This article focuses on a new and practical problem in single-sample per person face recognition (SSPP FR), i.e., SSPP FR with a contaminated biometric enrolment database (SSPP-ce FR), where the SSPP-based enrolment database is contaminated by nuisance facial variations in the wild, such as poor lightings, expression change, and disguises (e.g., we...
Article
Single sample per person face recognition (SSPP FR) is one of the most challenging problems in FR due to the extreme lack of enrolment data. To date, the most popular SSPP FR methods are the generic learning methods, which recognize query face images based on the so-called prototype plus variation (i.e., P+V) model. However, the classic P+V model s...
Article
Single sample per person (SSPP) face recognition with a contaminated biometric enrolment database (SSPP-ce FR) is an emerging practical FR problem, where the SSPP in the enrolment database is no longer standard but contaminated by nuisance facial variations such as expression, lighting, pose, and disguise. In this case, the conventional SSPP FR met...
Preprint
Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph-related tasks, e.g., node classification. However, recent works show that GNNs are vulnerable to evasion attacks, i.e., an attacker can perturb the graph structure to fool trained GNN models. Existing evasion attacks to GNNs have two key drawbacks. First, perturbi...
Preprint
Link prediction in dynamic graphs (LPDG) is an important research problem that has diverse applications such as online recommendations, studies on disease contagion, organizational studies, etc. Various LPDG methods based on graph embedding and graph neural networks have been recently proposed and achieved state-of-the-art performance. In this pape...
Conference Paper
Full-text available
Single sample face recognition is one of the most challenging problems in face recognition (FR), where only one single sample per person (SSPP) is enrolled in the gallery set for training. Although patch-based methods have achieved great success in FR with SSPP, they still have significant limitations. In this work, we propose a new patch-based met...
Article
Full-text available
Most off-the-shelf subspace learning methods directly calculate the statistical characteristics of the original input images, while ignoring different contributions of different image components. In fact, to extract efficient features for image analysis, the noise or trivial structure in images should have little contribution and the intrinsic stru...
Conference Paper
Full-text available
Traditional subspace learning methods directly calculate the statistical properties of the original input images, while ignoring different contributions of different image components. In fact, the noise (e.g., illumination, shadow) in the image often has a negative influence on learning the desired subspace and should have little contribution to im...
Article
Full-text available
Kernel Locality Preserving Projection (KLPP) algorithm can effectively preserve the neighborhood structure of the database using the kernel trick. We have known that supervised KLPP (SKLPP) can preserve within-class geometric structures by using label information. However, the conventional SKLPP algorithm endures the kernel selection which has sign...
Poster
Full-text available
This study aimed to improve the detection performance of movement related cortical potentials (MRCP) in EEG for brain-computer interface (BCI) applications. For this purpose, we apply an outlier-resisting manifold learning method to reduce false detection.
Article
Full-text available
In this paper, we propose a sparseness constraint NMF method, named graph regularized matrix factorization with sparse coding (GRNMF-SC). By combining manifold learning and sparse coding techniques together, GRNMF-SC can efficiently extract the basic vectors from the data space, which preserves the intrinsic manifold structure and also the local fe...
Article
Full-text available
One of the key issues of face recognition is to extract the features of face images. In this paper, we propose a novel method, named two-dimensional discriminant neighborhood preserving embedding (2DDNPE), for image feature extraction and face recognition. 2DDNPE benefits from four techniques, i.e., neighborhood preserving embedding (NPE), locality...
Conference Paper
Full-text available
Matrix factorization techniques have been frequently utilized in pattern recognition and machine learning. Among them, Non-negative Matrix Factorization (NMF) has received considerable attention because it represents the naturally occurring data by parts of it. On the other hand, from the geometric perspective, the data is usually sampled from a lo...
Article
Full-text available
Different kernels cause various class discriminations owing to their different geometrical structures of the data in the feature space. In this paper, a method of kernel optimization by maximizing a measure of class separability in the empirical feature space with sparse representation-based classifier (SRC) is proposed to solve the problem of auto...

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Projects (3)
Project
Recognize a person from a heterogeneous domain