Local binary pattern network: a deep learning approach for face recognition

Meng, Xiaofeng

doi:10.24124/2015/bpgub1134

Cited by 17 publications

(20 citation statements)

References 41 publications

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“…The most commonly used databases for face recognition systems under different conditions are Pointing Head Pose Image Database (PHPID) [126], Labeled Faces in Wild (LFW) [127], FERET [15,16], ORL, and Yale. The last are used for face recognition systems under different conditions, which provide information for supervised and unsupervised learning.…”

Section: Databases Usedmentioning

confidence: 99%

“…Khoi et al [20] propose a fast face recognition system based on LBP, pyramid of local binary pattern (PLBP), and rotation invariant local binary pattern (RI-LBP). Xi et al [15] have introduced a new unsupervised deep learning-based technique, called local binary pattern network (LBPNet), to extract hierarchical representations of data. The LBPNet maintains the same topology as the convolutional neural network (CNN).…”

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confidence: 99%

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Face Recognition Systems: A Survey

Kortli

Jridi²,

Falou³

et al. 2020

Sensors

422

146

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Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

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Section: Databases Usedmentioning

confidence: 99%

mentioning

confidence: 99%

Face Recognition Systems: A Survey

Kortli

Jridi²,

Falou³

et al. 2020

Sensors

422

146

View full text Add to dashboard Cite

show abstract

“…LHS [68] 0.8107 MRF-MLBP [69] 0.8994 SA-BSIF + WPCA [70] 0.9318 LBPNet [10] 0.9404 Pose Adaptive Filter (PAF) [71] 0.9405 Spartans [72] 0.9428 MRF-Fusion-CSKDA [73] 0.9894…”

Section: Methods Aucmentioning

confidence: 99%

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

2018

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Abstract:In this study, we propose a face recognition scheme using local Zernike moments (LZM), which can be used for both identification and verification. In this scheme, local patches around the landmarks are extracted from the complex components obtained by LZM transformation. Then, phase magnitude histograms are constructed within these patches to create descriptors for face images. An image pyramid is utilized to extract features at multiple scales, and the descriptors are constructed for each image in this pyramid. We used three different public datasets to examine the performance of the proposed method:Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), and Surveillance Cameras Face (SCface). The results revealed that the proposed method is robust against variations such as illumination, facial expression, and pose. Aside from this, it can be used for low-resolution face images acquired in uncontrolled environments or in the infrared spectrum. Experimental results show that our method outperforms state-of-the-art methods on FERET and SCface datasets.

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“…While the feature extraction based on the PCA filter does not fully consider the latent manifold structure of face images so well and lacks discriminant ability. Aside from PCANet, the filter-based multilayer network, either use the gabor filter [14], discrete cosine transform filter [15], LBP filter [16], etc., or linear discriminant analysis filter [17], canonical correlation analysis filter [18] in particular. One of the inspiring extensions in the recent is the CSGF(2D) 2 PCANet [19], which replace the PCA filter with CSGF filter, and has achieved surprising performance in extracting invariant and discriminative feature.…”

Section: Introductionmentioning

confidence: 99%

LMDAPNet: A Novel Manifold-Based Deep Learning Network

Cao

2020

IEEE Access

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In this paper, we propose a novel deep learning network, called Local Manifold Discriminant Analysis Projection Network (LMDAPNet). Different from most existing face recognition based on Deep Neural Networks (DNNs) methods that learn complex feature representations of data itself, our method aims to exploit the discriminant and geometrical structure of data manifold by optimally preserving the local neighborhood information. Firstly, it learns the local discriminant embedding for the submanifold of each class by solving an optimization problem. Secondly, we propose a Local Manifold Discriminant Analysis Projection (LMDAP) algorithm to learn convolutional kernel based on the local discriminant embedding space. Meanwhile, we define a new subspace-to-subspace distance metric to measure the dissimilarity between manifold pair. Finally, the learned convolutional kernel is used to extract feature maps efficiently. After a series of feature extraction, multiscale feature analysis is to encode the feature maps before feeding into a classifier. Experimental results on four benchmark datasets show that our proposed network is more robust and competitive performance for face recognition.

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Local binary pattern network: a deep learning approach for face recognition

Cited by 17 publications

References 41 publications

Face Recognition Systems: A Survey

Face Recognition Systems: A Survey

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

LMDAPNet: A Novel Manifold-Based Deep Learning Network

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