A novel LBP-based Color descriptor for face recognition

Lu, Ze; Jiang, Xudong; Kot, Alex C.

doi:10.1109/icassp.2017.7952478

Cited by 23 publications

(12 citation statements)

References 22 publications

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“…Even though some other color spaces achieve better performance than RGB color space, their pixel values are not quantitatively comparable. 5 Take YUV color space as an example, Y describes luminance information, whereas U and V describe chrominance information. 24 Thus Y, U, and V have totally different physical meanings and direct comparisons between pixel values of Y, U, and V may cause some problems.…”

Section: Local Ternary Patternmentioning

confidence: 99%

“…The mean of channel are v mean , similarly, where v mean ¼ ðv mean;1 ; v mean;2 ; v mean;3 Þ for three channels R, G, and B. The color distance of three channels is calculated between the pixel I p and its mean gray of each channel as follows: 20 E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 3 ; 6 3 ; 1 5…”

Section: Local Ternary Patternmentioning

confidence: 99%

“…The LBP method has been used widely due to the high performance and effective computation. It has been successfully applied to many computer fields, such as description of salient regions, 3 face recognition, 4,5 texture analysis, classification, [6][7][8] and so on. LBP has proven to be a robust and computationally simple approach to describe local structures.…”

Section: Introductionmentioning

confidence: 99%

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Image descriptor based on local color directional quaternionic pattern

Shan

Liu

2019

J. Electron. Imag.

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Most of the existing local binary pattern methods discard local color differences by holding their binary information. And local spatial information is also neglected. To address these problems, a robust color image descriptor local color directional quaternionic pattern (LCDQP) is proposed. In the descriptor, the color distance map (CDM) is generated in the RGB color space to capture the color distribution among three channels. According to the distribution of CDM elements and their neighbors, four edge models are defined to describe the change trend of the original image. Then, based on this, the LCDQP strings are gained according to the distribution of four models in the four directions of 0 deg, 45 deg, 90 deg, and 135 deg. Finally, an effective quaternionic code method is adopted to construct the LCDQP descriptor. The proposed descriptor not only captures the local color features but also reflects the spatial structure information. Experiments on four representative databases demonstrate that the proposed descriptor is superior to other state-of-the-art approaches. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Local Ternary Patternmentioning

confidence: 99%

Section: Local Ternary Patternmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image descriptor based on local color directional quaternionic pattern

Shan

Liu

2019

J. Electron. Imag.

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“…Traditional face detection methods are mostly used for single face matching in a simple background [5]. For example, the PCA method [6] is used to extract facial features; serial and parallel methods are used to combine the extracted facial features [7]; and the LBP pattern is widely used for face recognition [8][9][10]. Due to the limitations of traditional face detection algorithms, it is usually effective to detect a single face in a specific environment, but the accuracy of face recognition for a dense small scale is low.…”

Section: Introductionmentioning

confidence: 99%

SE-IYOLOV3: An Accurate Small Scale Face Detector for Outdoor Security

et al. 2020

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Small scale face detection is a very difficult problem. In order to achieve a higher detection accuracy, we propose a novel method, termed SE-IYOLOV3, for small scale face in this work. In SE-IYOLOV3, we improve the YOLOV3 first, in which the anchorage box with a higher average intersection ratio is obtained by combining niche technology on the basis of the k-means algorithm. An upsampling scale is added to form a face network structure that is suitable for detecting dense small scale faces. The number of prediction boxes is five times more than the YOLOV3 network. To further improve the detection performance, we adopt the SENet structure to enhance the global receptive field of the network. The experimental results on the WIDERFACEdataset show that the IYOLOV3 network embedded in the SENet structure can significantly improve the detection accuracy of dense small scale faces.

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“…For color information, there have been some scholars [ 42 , 43 , 44 ] who have noticed its importance to face recognition. Color face recognition is usually conducted on each color channel and the final result comes from the fusion of the three color channels.…”

Section: Introductionmentioning

confidence: 99%

Robust Face Recognition Using the Deep C2D-CNN Model Based on Decision-Level Fusion

Qiu

Wen

et al. 2018

Sensors

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Given that facial features contain a wide range of identification information and cannot be completely represented by a single feature, the fusion of multiple features is particularly significant for achieving a robust face recognition performance, especially when there is a big difference between the test sets and the training sets. This has been proven in both traditional and deep learning approaches. In this work, we proposed a novel method named C2D-CNN (color 2-dimensional principal component analysis (2DPCA)-convolutional neural network). C2D-CNN combines the features learnt from the original pixels with the image representation learnt by CNN, and then makes decision-level fusion, which can significantly improve the performance of face recognition. Furthermore, a new CNN model is proposed: firstly, we introduce a normalization layer in CNN to speed up the network convergence and shorten the training time. Secondly, the layered activation function is introduced to make the activation function adaptive to the normalized data. Finally, probabilistic max-pooling is applied so that the feature information is preserved to the maximum extent while maintaining feature invariance. Experimental results show that compared with the state-of-the-art method, our method shows better performance and solves low recognition accuracy caused by the difference between test and training datasets.

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A novel LBP-based Color descriptor for face recognition

Cited by 23 publications

References 22 publications

Image descriptor based on local color directional quaternionic pattern

Image descriptor based on local color directional quaternionic pattern

SE-IYOLOV3: An Accurate Small Scale Face Detector for Outdoor Security

Robust Face Recognition Using the Deep C2D-CNN Model Based on Decision-Level Fusion

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