The Face Recognition Method Based on CS-LBP and DBN

Sun, Kun; Yin, Xin; Yang, Minghong; Yang, Wang; Fan, Jianying

doi:10.1155/2018/3620491

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…This proposed approach can be utilized for components-based face recognition, partial face recognition and full face recognition because of the alliance among face and its components. Sun, Kun et al [8], this paper presents centre-symmetric local binary pattern and DBN method for face recognition. Initially image is separated into blocks, and feature is extracted from each block using CS-LBP, then texture feature histogram is calculated.…”

Section: Related Workmentioning

confidence: 99%

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Texture Analytics for Accurate Person Recognition: A Multimodal Approach

Suchetha¹,

M²

2023

IJACSA

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Securing the resources is a most challenging task in the digital era. Traditionally, password and ID card systems were used to provide security. Password and ID cards can be stolen or hacked; to overcome this drawback biometric systems are used to authenticate the user to access the data or resources. Biometric system uses physical and behavioral characteristics of the user. Biological characteristics of the person like face, fingerprint, iris, palm print, voice, hand geometry etc. cannot be stolen and misused. Even though unimodal biometric system is more secure as compared to the traditional approach, it is not able to handle intra-class, inter-class variations, noisy data and spoofing attack. These problems can be solved using multimodal biometrics. In this paper, we discuss unimodal biometric system using Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). We propose a feature level fusion of face and fingerprint biometric traits using LTP. The implementation of the introduced system stands in comparison to the unimodal LBP and LTP for face and fingerprint system. The system is tested on ORL, UMIST, VISA face dataset and FVC fingerprint dataset. Experimental results show that the multimodal biometric system using LTP gives better accuracy as compared to the unimodal biometric system.

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Section: Related Workmentioning

confidence: 99%

“…The neighbour is estimated using bilinear interpolation when it is not falling exactly in the centre of the pixel. After this encoding step, using following equation histogram is created using equation ( 7) and (8).…”

Section: B Local Ternary Patternmentioning

confidence: 99%

Texture Analytics for Accurate Person Recognition: A Multimodal Approach

Suchetha¹,

M²

2023

IJACSA

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“…典型的代表方法有方向梯度直方图(histogram of oriented gradient, HOG) [6] 、Gabor 变换 [7] 、局部三值模式(local ternary pattern, LTP) [8] 、局部相位量化(local phase quantization, LPQ) [9] 和局部二值模式(local binary pattern, LBP) [10] . 在这些方法中, LBP 及其衍生方法 [11][12][13][14][15][16] 因其具有高性能和计算复杂度低的特点, 近些年受到了广泛的关注.…”

Section: 人脸识别大多数研究中都使用了纹理分析和分类纹理反映了图像的视觉特征表征一个物体unclassified

Face Recognition with Local High-Order Principal Direction Pattern Based on “Gradient Face”

Ye¹,

Wang²,

Ying³

et al. 2021

J Comput-Aided Design Comput Graphics

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Pointing to weak robustness caused by the noise sensitivity and feature redundancy of present face recognition methods with high-order features, a new method of the local high-order principal direction pattern based on "gradient face" is proposed. Firstly, the gradient face convolution operator designed is used to compute the sum of multi-directional gradient components of pixels to construct a gradient face. Then, the principal direction grouping strategy is introduced on the gradient face to characterize its high-order derivative features, and a principal direction feature map is formed according to the feature code of high-order derivatives direction changes in local neighborhood. Finally, block statistics and cascading of histogram features are made a vector to be input in to a support vector machine for multi-classification. Experimental results of several public face databases show that the proposed method is robust to changes in illumination, expression, and facial occlusion and has higher recognition efficiency.

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“…The data is insufficient and the usability needs to be excavated. Sun et al [8] proposed a face recognition method based on center-symmetric local binary pattern (CS-LBP) and DBN (FRMCD). The results show that the recognition rate of FRMCD is superior to that of traditional methods; the highest recognition rate is up to 98.82%.…”

Section: Introductionmentioning

confidence: 99%

Image Recognition Method for Pitching Fingers of Basketball Players Based on Symmetry Algorithm

Chen

2021

Wireless Communications and Mobile Computing

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In the basketball game, the accuracy and standardization of pitching are directly related to the score. So it is very important to analyze the pitching figure movement to have a better positioning of the fingers. There are limited techniques to recognize the movement. The human motion recognition method is one of them. It utilizes the spatiotemporal image segmentation and interactive region detection to recognize images of pitching finger movement of basketball players. This method has a limitation that the symmetrical information of the human body and sphere cannot be excavated, which leads to certain errors in recognition effect. This paper presents a method of recognizing pitching finger movement of basketball players based on symmetry algorithm, constructs an acquisition model, carries out edge contour detection and adaptive feature segmentation of images of pitching finger movement of basketball players, and uses a fixed threshold to segment finger image to extract players’ hand contour and locate the middle axis of the finger. On this basis, the symmetry recognition method based on nematode recognition algorithm is used to recognize the symmetry of basketball pitching finger movement image and complete the accurate recognition of basketball pitching finger movement image. The experimental results show that the proposed method can effectively recognize the basketball player’s finger movement image. The average recognition accuracy is 98%, the growth rate of recognition speed is 98%, and the maximum recognition time is 12 s. The robustness of the proposed method is 0.45.

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The Face Recognition Method Based on CS-LBP and DBN

Cited by 7 publications

References 24 publications

Texture Analytics for Accurate Person Recognition: A Multimodal Approach

Texture Analytics for Accurate Person Recognition: A Multimodal Approach

Face Recognition with Local High-Order Principal Direction Pattern Based on “Gradient Face”

Image Recognition Method for Pitching Fingers of Basketball Players Based on Symmetry Algorithm

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The Face Recognition Method Based on CS-LBP and DBN

Cited by 7 publications

References 24 publications

Texture Analytics for Accurate Person Recognition: A Multimodal Approach

Texture Analytics for Accurate Person Recognition: A Multimodal Approach

Face Recognition with Local High-Order Principal Direction Pattern Based on &ldquo;Gradient Face&rdquo;

Image Recognition Method for Pitching Fingers of Basketball Players Based on Symmetry Algorithm

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Product

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Face Recognition with Local High-Order Principal Direction Pattern Based on “Gradient Face”