scholarly journals The Face Recognition Method Based on CS-LBP and DBN

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Kun Sun ◽  
Xin Yin ◽  
Mingxin Yang ◽  
Yang Wang ◽  
Jianying Fan
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Texture Feature ◽  
Texture Features ◽  
Face Image ◽  
Recognition Method ◽  
Traditional Methods ◽  
Face Images ◽  
The Face

At present, the face recognition method based on deep belief network (DBN) has advantages of automatically learning the abstract information of face images and being affected slightly by active factors, so it becomes the main method in the face recognition area. Because DBN ignores the local information of face images, the face recognition rate based on DBN is badly affected. To solve this problem, a face recognition method based on center-symmetric local binary pattern (CS-LBP) and DBN (FRMCD) is proposed in this paper. Firstly, the face image is divided into several subblocks. Secondly, CS-LBP is used to extract texture features of each image subblock. Thirdly, texture feature histograms are formed and input into the DBN visual layer. Finally, face classification and face recognition are completed through deep learning in DBN. Through the experiments on face databases ORL, Extend Yale B, and CMU-PIE by the proposed method (FRMCD), the best partitioning way of the face image and the hidden unit number of the DBN hidden layer are obtained. Then, comparative experiments between the FRMCD and traditional methods are performed. The results show that the recognition rate of FRMCD is superior to those of traditional methods; the highest recognition rate is up to 98.82%. When the number of training samples is less, the FRMCD has more significant advantages. Compared with the method based on local binary pattern (LBP) and DBN, the time-consuming of FRMCD is shorter.

Face Recognition by PCA and Improved LBP Fusion Algorithm

2015 ◽  
Vol 734 ◽  
pp. 562-567 ◽  
Author(s):  
En Zeng Dong ◽  
Yan Hong Fu ◽  
Ji Gang Tong
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Principal Component ◽  
Texture Features ◽  
Rotation Invariant ◽  
Fusion Algorithm ◽  
Face Images ◽  
The Face ◽  
Pca Method

This paper proposed a theoretically efficient approach for face recognition based on principal component analysis (PCA) and rotation invariant uniform local binary pattern texture features in order to weaken the effects of varying illumination conditions and facial expressions. Firstly, the rotation invariant uniform LBP operator was adopted to extract the local texture feature of the face images. Then PCA method was used to reduce the dimensionality of the extracted feature and get the eigenfaces. Finally, the nearest distance classification was used to distinguish each face. The method has been accessed on Yale and ATR-Jaffe face databases. Results demonstrate that the proposed method is superior to standard PCA and its recognition rate is higher than the traditional PCA. And the proposed algorithm has strong robustness against the illumination changes, pose, rotation and expressions.

scholarly journals Unrestricted Face Recognition Algorithm Based on Transfer Learning on Self-Pickup Cabinet

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhixue Liang
Keyword(s):  
Face Recognition ◽  
Transfer Learning ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Face Image ◽  
Delivery Device ◽  
Face Images ◽  
Region Extraction ◽  
The Face ◽  
Enhancement Algorithm

In the contactless delivery scenario, the self-pickup cabinet is an important terminal delivery device, and face recognition is one of the efficient ways to achieve contactless access express delivery. In order to effectively recognize face images under unrestricted environments, an unrestricted face recognition algorithm based on transfer learning is proposed in this study. First, the region extraction network of the faster RCNN algorithm is improved to improve the recognition speed of the algorithm. Then, the first transfer learning is applied between the large ImageNet dataset and the face image dataset under restricted conditions. The second transfer learning is applied between face image under restricted conditions and unrestricted face image datasets. Finally, the unrestricted face image is processed by the image enhancement algorithm to increase its similarity with the restricted face image, so that the second transfer learning can be carried out effectively. Experimental results show that the proposed algorithm has better recognition rate and recognition speed on the CASIA-WebFace dataset, FLW dataset, and MegaFace dataset.

Neural Network-Based Approach for Face Recognition Across Varying Pose

2015 ◽  
Vol 29 (08) ◽  
pp. 1556015 ◽  
Author(s):  
Seyed Omid Shahdi ◽  
S. A. R. Abu-Bakar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Local Information ◽  
Discrete Wavelet ◽  
Face Images ◽  
The Face ◽  
The Relationship

At present, frontal or even near frontal face recognition problem is no longer considered as a challenge. Recently, the shift has been to improve the recognition rate for the nonfrontal face. In this work, a neural network paradigm based on the radial basis function approach is proposed to tackle the challenge of recognizing faces in different poses. Exploiting the symmetrical properties of human face, our work takes the advantage of the existence of even half of the face. The strategy is to maximize the linearity relationship based on the local information of the face rather than on the global information. To establish the relationship, our proposed method employs discrete wavelet transform and multi-color uniform local binary pattern (ULBP) in order to obtain features for the local information. The local information will then be represented by a single vector known as the face feature vector. This face feature vector will be used to estimate the frontal face feature vector which will be used for matching with the actual vector. With such an approach, our proposed method relies on a database that contains only single frontal face images. The results shown in this paper demonstrate the robustness of our proposed method even at low-resolution conditions.

scholarly journals Extended Local Binary Pattern Features based Face Recognition using Multilevel SVM Classifier

2019 ◽  
Vol 8 (3) ◽  
pp. 4123-4128
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Svm Classifier ◽  
Recognition Method ◽  
Face Area ◽  
Face Images ◽  
The Face ◽  
Zero Degree ◽  
The Individual ◽  
Biometric Trait

The Face recognition method is one of the authoritative biometric system in recognition methods to recognize the individual, because face is a distinctive biometric trait of an human being and it is the superior method of recognition. This paper proposes a novel Face recognition method by using extended LBP features. The pre-processing is carried out to extract the face area using viola-johns algorithm and all images are resized to 100x100. The LBP operator is applied on resized face images by rotating the each image by 15 degrees, i.e., at 7 degree left and 7 degree right and at zero degree to extract the feature vectors and final features are obtained by applying histogram technique. The SVM classifier is used for matching the database images with test images to measure the performance such as TSR, FAR, FRR & EER. The performance parameters are compared with existing algorithms for YALE and FERET database.

scholarly journals Texture Characteristic of Local Binary Pattern on Face Recognition with Probabilistic Linear Discriminant Analysis

2020 ◽  
Vol 1 (1) ◽  
pp. 22-26
Author(s):  
Isnawati Muslihah ◽  
Muqorobin Muqorobin
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Local Binary Pattern ◽  
Face Image ◽  
Image Texture ◽  
Identification System ◽  
Linear Discriminant ◽  
Face Images ◽  
The Face

Face recognition is an identification system that uses the characteristics of a person's face for processing. There is a feature in the face image so that it can be distinguished between one face and another face. One way to recognize face images is to analyze the texture of the face image. Texture analysis generally requires a feature extraction process. In different images, the characteristics will also differ. This characteristic will be the basis for the recognition of facial images. However, existing face recognition methods experience efficiency problems and rely heavily on the extraction of the right features. This study aims to study the texture characteristics of the extraction results using the Local Binary Pattern (LBP) method which is applied to deal with the introduction of Probabilistic Linear Discriminant Analysis (PLDA). The data used in this study are human face images from the AR Faces database, consisting of 136 objects (76 men and 60 women), each of which has 7 types of images Based on the results of testing shows the LBP method can produce the highest accuracy with a value of 95.53% in the introduction of PLDA.

scholarly journals A novel approach for Face Recognition using Local Binary Pattern

2012 ◽  
pp. 25-31
Author(s):  
Sonal R. Ahirrao ◽  
D. S. Bormane
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Global Features ◽  
Face Images ◽  
Grey Scale ◽  
Novel Approach ◽  
The Face ◽  
Scale Transformations ◽  
Micro Patterns

This paper presents Local Binary pattern (LBP) as an approach for face recognition with the use of some global features also. Face recognition has received quite a lot of attention from researchers in biometrics, pattern recognition, and computer vision communities. The idea behind using the LBP features is that the face images can be seen as composition of micro-patterns which are invariant with respect to monotonic grey scale transformations and robust to factors like ageing. Combining these micro-patterns, a global description of the face image is obtained. Efficiency and the simplicity of the proposed method allows for very fast feature extraction giving better accuracy than the other algorithms. The proposed method is tested and evaluated on ORL datasets combined with other university dataset to give a good recognition rate and 89% classification accuracy using LBP only and 98% when global features are combined with LBP. The method is also tested for real images to give good accuracy and recognition rate. The experimental results show that the method is valid and feasible.

scholarly journals Masked Face Recognition Algorithm for a Contactless Distribution Cabinet

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
GuiLing Wu
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Face Image ◽  
Attention Mechanism ◽  
Human Contact ◽  
Face Images ◽  
The Face ◽  
Common Application ◽  
Image Part

A contactless delivery cabinet is an important courier self-pickup device, for the reason that COVID-19 can be transmitted by human contact. During the pandemic period of COVID-19, wearing a mask to take delivery is a common application scenario, which makes the study of masked face recognition algorithm greatly significant. A masked face recognition algorithm based on attention mechanism is proposed in this paper in order to improve the recognition rate of masked face images. First, the masked face image is separated by the local constrained dictionary learning method, and the face image part is separated. Then, the dilated convolution is used to reduce the resolution reduction in the subsampling process. Finally, according to the important feature information of the face image, the attention mechanism neural network is used to reduce the information loss in the subsampling process and improve the face recognition rate. In the experimental part, the RMFRD and SMFRD databases of Wuhan University were selected to compare the recognition rate. The experimental results show that the proposed algorithm has a better recognition rate.

Gabor-oriented local order feature-based deep learning for face annotation

2019 ◽  
Vol 17 (05) ◽  
pp. 1950032
Author(s):  
A. Kasthuri ◽  
A. Suruliandi ◽  
S. P. Raja
Keyword(s):  
Semantic Network ◽  
Real Life ◽  
Texture Feature ◽  
Texture Features ◽  
Feature Representation ◽  
Local Order ◽  
Face Images ◽  
Novel Approach ◽  
The Face ◽  
Feature Based

Face annotation, a modern research topic in the area of image processing, has useful real-life applications. It is a really difficult task to annotate the correct names of people to the corresponding faces because of the variations in facial appearance. Hence, there still is a need for a robust feature to improve the performance of the face annotation process. In this work, a novel approach called the Deep Gabor-Oriented Local Order Features (DGOLOF) for feature representation has been proposed, which extracts deep texture features from face images. Seven recently proposed face annotation methods are considered to evaluate the proposed deep texture feature under uncontrolled situations like occlusion, expression changes, illumination and pose variations. Experimental results on the LFW, IMFDB, Yahoo and PubFig databases show that the proposed deep texture feature provides efficient results with the Name Semantic Network (NSN)-based face annotation. Moreover, it is observed that the proposed deep texture feature improves the performance of face annotation, regardless of all the challenges involved.

scholarly journals 3D Face Image Inpainting with Generative Adversarial Nets

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Tongxin Wei ◽  
Qingbao Li ◽  
Jinjin Liu ◽  
Ping Zhang ◽  
Zhifeng Chen
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Image Inpainting ◽  
Face Image ◽  
Edge Information ◽  
3D Face ◽  
Face Images ◽  
The Face ◽  
Match Effect ◽  
Traditional Image

In the process of face recognition, face acquisition data is seriously distorted. Many face images collected are blurred or even missing. Faced with so many problems, the traditional image inpainting was based on structure, while the current popular image inpainting method is based on deep convolutional neural network and generative adversarial nets. In this paper, we propose a 3D face image inpainting method based on generative adversarial nets. We identify two parallels of the vector to locate the planer positions. Compared with the previous, the edge information of the missing image is detected, and the edge fuzzy inpainting can achieve better visual match effect. We make the face recognition performance dramatically boost.

The Safety Research for Face Recognition System Based on Image Feature and Digital Watermarking

2012 ◽  
Vol 224 ◽  
pp. 485-488
Author(s):  
Fei Li ◽  
Yuan Yuan Wang
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Face Image ◽  
Image Feature ◽  
Safety Research ◽  
The Face ◽  
Information Image ◽  
Image Integrity ◽  
Recognition Software

Abstract: In order to solve the easily copied problem of images in face recognition software, an algorithm combining the image feature with digital watermark is presented in this paper. As watermark information, image feature of the adjacent blocks are embedded to the face image. And primitive face images are not needed when recovering the watermark. So face image integrity can be well confirmed, and the algorithm can detect whether the face image is the original one and identify whether the face image is attacked by malicious aim-such as tampering, replacing or illegally adding. Experimental results show that the algorithm with good invisibility and excellent robustness has no interference on face recognition rate, and it can position the specific tampered location of human face image.

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