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Author

Hung Phuoc Truong

Bio: Hung Phuoc Truong is an academic researcher from Sejong University. The author has contributed to research in topics: Facial recognition system & Subspace topology. The author has an hindex of 3, co-authored 9 publications receiving 26 citations. Previous affiliations of Hung Phuoc Truong include Ho Chi Minh City University of Science.

Papers
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Book ChapterDOI
24 Sep 2018
TL;DR: Experiments show that EL-LBP outperforms previous LBP methods in terms of recognition accuracy with much lower time cost, suggesting that this new representation scheme would be more powerful in the embedded vision systems where the computational cost is critical.
Abstract: Local Binary Patterns (LBP) is one of the efficient approaches for image representation, especially in the face recognition field. The motivation of the present study is to find a compact descriptor which captures texture information and yet is robust against several visual challenges such as illumination variation, facial expressions and head pose variation. The proposed approach, called it Enhance Line Local Binary Patterns (EL-LBP), is an improvement of 1D-Local Binary Patterns (1D-LBP) by reducing the dimension of feature vectors within 1D-LBP histogram and it leads to decrease the time cost during the matching stage. Experiments using ORL, Yale and AR datasets show that EL-LBP outperforms previous LBP methods in terms of recognition accuracy with much lower time cost, suggesting that this new representation scheme would be more powerful in the embedded vision systems where the computational cost is critical.

8 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper presented two shape signature-based reflection symmetry detection methods with their theoretical underpinning and empirical evaluation, which can effectively deal with compound shapes which are challenging for traditional contour-based methods.

8 citations

Journal ArticleDOI
TL;DR: A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 99.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively.
Abstract: We present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.

8 citations

Proceedings ArticleDOI
26 Feb 2015
TL;DR: The initial authentication exchange in Kerberos 5 is modified by using biometric data and asymmetric cryptography to create a new preauthentication protocol in order to make Kerbero 5 more secure.
Abstract: Kerberos is a well-known network authentication protocol that allows nodes to communicate over a non-secure network connection. After Kerberos is used to prove the identity of objects in client-server model, it will encrypt all of their communications in following steps to assure privacy and data integrity. In this paper, we modify the initial authentication exchange in Kerberos 5 by using biometric data and asymmetric cryptography. This proposed method creates a new preauthentication protocol in order to make Kerberos 5 more secure. Due to the proposed method, the limitation of password-based authentication in Kerberos 5 is solved. It is too difficult for a user to repudiate having accessed to the application. Moreover, the mechanism of user authentication is more convenient. This method is a strong authentication scheme that is against several attacks.

6 citations

Proceedings ArticleDOI
05 Dec 2013
TL;DR: This work introduces the theory of 2D-FPCA based on the definition of fractional variance and fractional covariance matrix and shows its improvement called Bilateral Fractional Principle Component Analysis, and proves the stability and robustness of the proposed framework.
Abstract: A novel approach based on structure information extraction in frequency domain is proposed for image representation problem. Regarding this problem, a new subspace method based on Two-dimensional Fractional Principle Component Analysis (2D-FPCA) in frequency domain is applied to images, thus extracting the texture information. In order to extract the structure information, the system utilizes this new subspace as the bilateral consideration of 2D-FPCA technique called B2D-FPCA. For this purpose: (1) we first introduce the theory of 2D-FPCA based on the definition of fractional variance and fractional covariance matrix; (2) then show its improvement called Bilateral 2D-FPCA and (3) the robustness of 2D-DCT is also described as the preprocessing step. This approach is applied to facial expression representation problem to prove the stability and robustness of the proposed framework. For demonstration, facial expressions datasets (JAFFE, Pain expression subset and Cohn-Kanade) are used in order to compare the proposed framework with some other approaches.

5 citations


Cited by
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01 Jan 2016
TL;DR: The handbook of biometrics is universally compatible with any devices to read, and will help you to get the most less latency time to download any of the authors' books like this one.
Abstract: Thank you very much for reading handbook of biometrics. Maybe you have knowledge that, people have look numerous times for their favorite books like this handbook of biometrics, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some harmful virus inside their desktop computer. handbook of biometrics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the handbook of biometrics is universally compatible with any devices to read.

275 citations

Proceedings ArticleDOI
01 Sep 2015
TL;DR: The designed system is very effective and ecofriendly having the advantage of low cost, security has been increased in this system on the server side by using 3 level Kerberos authentication and the system is now more secure to use than the current smart homes systems.
Abstract: Uses of Internet-of-Things have been increased almost in all domains. Smart Home System can be made using Internet-of-Things. This paper presents the design and an effective implementation of smart home system using Internet of things. The designed system is very effective and ecofriendly having the advantage of low cost. This system ease out the home automation task and user can easily monitor control home appliances from anywhere and anytime using internet. Embedded system, GPRS module and RF modules are used for making this system. Security has been increased in this system on the server side by using 3 level Kerberos authentication. Hence, the system is now more secure to use than the current smart homes systems. Design of hardware and software is also presented in paper.

26 citations

Proceedings ArticleDOI
01 Sep 2016
TL;DR: A new fusion method based on CNN and support vector machines for facial expression recognition problem which is more efficient than CNN only and investigated on Cohn-Kanade dataset achieves 96.04% in accuracy rate.
Abstract: Motivated by the newly recent trend in pattern recognition — convolutional neural network (CNN), we introduce a new fusion method based on CNN and support vector machines (SVM) for facial expression recognition problem Our study puts the deep generic features from CNN and SVM together which is more efficient than CNN only We investigate our proposed method on Cohn-Kanade dataset and achieve 9604% in accuracy rate which is better than other state-of-the-art methods

17 citations

Journal ArticleDOI
TL;DR: The DR_LBP approach is proposed to address the failure of the original LBP by defining distances and using some of them in a ratio form to extract more discriminative features that lead to more accurate classification.
Abstract: Face recognition applications focus on local features to prevent detailed information from being omitted while the feature extraction processes. This paper is based on presenting a local pattern-based model to extract more discriminative features that lead to more accurate classification. In local pattern-based feature extraction, the LBP is one of the most important approaches that many variants of this method have been proposed till now. LBP calculation is based on differences between the central pixel and the desired one. In contrast, the information hidden in the selected pixel’s neighborhood pixels is not included in this process. This paper proposes the DR_LBP approach to address this failure by defining distances and using some of them in a ratio form. Successful results have been earned in many experimental results. In LBP, the calculations’ primary flow takes advantage of two pixels in the LBP box, the central and the desired pixel. Contrary to the original LBP, this paper’s proposed approach uses three pixels of LBP box to conduct the feature vector, which leads to employing the information hidden in the relationship between neighboring pixels. This approach applies the experiments on two standard datasets, ORL Yale face and Faces94 dataset. The accuracy percent of the proposed plan is 95.95, 94.09 and 98.01 on ORL, Yale face and Faces94 dataset, respectively, which is the reason to present this model as a new face feature extraction approach.

13 citations

Book ChapterDOI
24 Sep 2018
TL;DR: Experiments show that EL-LBP outperforms previous LBP methods in terms of recognition accuracy with much lower time cost, suggesting that this new representation scheme would be more powerful in the embedded vision systems where the computational cost is critical.
Abstract: Local Binary Patterns (LBP) is one of the efficient approaches for image representation, especially in the face recognition field. The motivation of the present study is to find a compact descriptor which captures texture information and yet is robust against several visual challenges such as illumination variation, facial expressions and head pose variation. The proposed approach, called it Enhance Line Local Binary Patterns (EL-LBP), is an improvement of 1D-Local Binary Patterns (1D-LBP) by reducing the dimension of feature vectors within 1D-LBP histogram and it leads to decrease the time cost during the matching stage. Experiments using ORL, Yale and AR datasets show that EL-LBP outperforms previous LBP methods in terms of recognition accuracy with much lower time cost, suggesting that this new representation scheme would be more powerful in the embedded vision systems where the computational cost is critical.

8 citations