Collaborative similarity metric learning for face recognition in the wild

Gundogdu, Batuhan; Bianco, Michael J.

doi:10.1049/iet-ipr.2019.0510

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…The positions of the seven feature points are obviously different from the gray features of other parts of the face. Therefore, face features can be collected quickly through integral projection [28,29]. The gray image can be processed directly by integral projection, or the image can be binarized and then integrated projection.…”

Section: Realize the Fast Face Recognition Methodsmentioning

confidence: 99%

Research on Fast Face Recognition Method Based on Double Decision Subspace

Wang

Zhang

Huang

2022

Wireless Communications and Mobile Computing

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Because a large number of labeled face data samples in special scenes need a large number of training samples with identity markers, and it is impossible to accurately extract the characteristics of small samples, a fast face recognition method based on double decision subspace is proposed. A feature recognition structure based on double decision subspace is constructed to preprocess the face image and separate the local features of several corresponding face images. The local binary pattern is used to extract the local texture features of the face, and the deep convolution network face fast recognition model is constructed. The convolution network is used to share the weight, pool, and downsampling to reduce the complexity of the model. The constructed recognition model is used to recognize the features of the face image, and the fast face recognition is effectively completed. The experimental results show that the designed method has high recognition accuracy, less average recognition time, and good recognition performance.

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Section: Realize the Fast Face Recognition Methodsmentioning

confidence: 99%

Research on Fast Face Recognition Method Based on Double Decision Subspace

Wang

Zhang

Huang

2022

Wireless Communications and Mobile Computing

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“…MJEN MANAS Journal of Engineering, Volume 9 (Issue 1) © 2021 www.journals.manas.edu.kg gender recognition [15], edge detection for noisy images [16], breast tumor diagnosis [17], texture image retrieval [18], face similarity comparison [19], color texture recognition [20]. Most deep learning or machine learning models for computer vision applications like image LBP demand intense computational power.…”

Section: Introductionmentioning

confidence: 99%

A TensorFlow implementation of Local Binary Patterns Transform

Akgün

2021

MANAS Journal of Engineering

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Feature extraction layers like Local Binary Patterns (LBP) transform can be very useful for improving the accuracy of machine learning and deep learning models depending on the problem type. Direct implementations of such layers in Python may result in long running times, and training a computer vision model may be delayed significantly. For this purpose, TensorFlow framework enables developing accelerated custom operations based on the existing operations which already have support for accelerated hardware such as multicore CPU and GPU. In this study, LBP transform which is used for feature extraction in various applications, was implemented based on TensorFlow operations. The evaluations were done using both standard Python operations and TensorFlow library for performance comparisons. The experiments were realized using images in various dimensions and various batch sizes. Numerical results show that algorithm based on TensorFlow operations provides good acceleration rates over Python runs. The implementation of LBP can be used for the accelerated computing for various feature extraction purposes including machine learning as well as in deep learning applications.

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“…Face alignment, which is also called facial landmark localisation, aims to locate the facial landmarks (the points around eyebrows, eyes, nose, mouth and contour) based on face detection. It is also an essential part of face image processing, and plays an important role in face recognition [1], expression analysis [2], 3D face reconstruction [3] and so on. At present, most of the face alignment methods have achieved satisfactory results on the frontal face images.…”

Section: Introductionmentioning

confidence: 99%

Face alignment based on fusion subspace and 3D fitting

Zhang

Liang

2020

IET Image Processing

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The traditional face alignment approaches based on cascade regression have achieved satisfactory result on the frontal face, but for the face with large changes in posture and expression, a single initial shape will lead to the result falling into local optimum. In order to solve this problem, a two‐stage cascade regression model for face alignment is proposed, which generates coarse initial shape from the aligned salient shape. The first stage is used to align the salient shape that contains some prominent landmarks. To enhance the robustness of authors' method, the fusion subspace is used to divide the samples, and each subset trains cascade regression model separately. The alignment results of the first stage are used to generate the coarse initial shapes for the second stage through 3D fitting. The second stage is still based on cascade regression, which is used to further predict the full shape. The experimental results demonstrate the proposed method can achieve state‐of‐art performance, especially in unconstrained conditions with various poses.

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Collaborative similarity metric learning for face recognition in the wild

Cited by 6 publications

References 33 publications

Research on Fast Face Recognition Method Based on Double Decision Subspace

Research on Fast Face Recognition Method Based on Double Decision Subspace

A TensorFlow implementation of Local Binary Patterns Transform

Face alignment based on fusion subspace and 3D fitting

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