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.

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 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.

scholarly journals Face Spoofing Detection using Enhanced Local Binary Pattern

2019 ◽  
Vol 9 (2) ◽  
pp. 3365-3371
Keyword(s):  
Neural Network ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Substantial Improvement ◽  
Novel Approach ◽  
The Face ◽  
The Difference ◽  
Lbp Descriptor ◽  
Face Spoofing ◽  
Made In

Among various biometric systems, over the past few years identifying the face patterns has become the centre of attraction, owing to this, a substantial improvement has been made in this area. However, the security of such systems may be a crucial issue since it is proved in many studies that face identification systems are susceptible to various attacks, out of which spoofing attacks are one of them. Spoofing is defined as the capability of making fool of a system that is biometric for finding out the unauthorised customers as an actual one by the various ways of representing version of synthetic forged of the original biometric trait to the sensing objects. In order to guard face spoofing, several anti-spoofing methods are developed to do liveliness detection. Various techniquesfordetection of spoofing make the use of LBP i.e. local binary patterns that make the difference to symbolise handcrafted texture features from images, whereas, recent researches have shown that deep features are more robust in comparison to the former one. In this paper, a proper countermeasure in opposite to attacks that are on face spoofing are relied on CNN i.e. Convolutional Neural Network. In this novel approach, deep texture features from images are extracted by integrating the modified version of LBP descriptor (Gene LBP net) to a CNN. Experimental results are obtained on NUAA spoofing database which defines that these deep neural network surpass most of the state-of-the-art techniques, showing good outcomes in context to finding out the criminal attacks

scholarly journals A Revisit Histogram of Oriented Descriptor for Facial Color Image Classification Based on Fusion of Color Information

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huy Nguyen-Quoc ◽  
Vinh Truong Hoang
Keyword(s):  
Color Image ◽  
Real Life ◽  
Selection Method ◽  
Color Images ◽  
Human Detection ◽  
Maximum Magnitude ◽  
Color Component ◽  
Novel Approach ◽  
The Face ◽  
Benchmark Datasets

Histogram of Oriented Gradient (HOG) is a robust descriptor which is widely used in many real-life applications, including human detection, face recognition, object counting, and video surveillance. In order to extract HOG descriptor from color images whose information is three times more than the grayscale images, researchers currently apply the maximum magnitude selection method. This method makes the information of the resulted image is reduced by selecting the maximum magnitudes. However, after we extract HOG using the unselected magnitudes of the maximum magnitude selection method, we observe that the performance is better than using the maximum magnitudes in several cases. Therefore, in this paper, we propose a novel approach for extracting HOG from color images such as Color Component Selection and Color Component Fusion. We also propose the extended kernels in order to improve the performance of HOG. With our new approaches in the color component analysis, the experimental results of several facial benchmark datasets are enhanced with the increment from 3 to 10% of accuracy. Specifically, a 95.92% of precision is achieved on the Face AR database and 75% on the Georgia Face database. The results are better more than 10 times compared with the original HOG approach.

scholarly journals A Random Forest and Current Fault Texture Feature–Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR

2021 ◽  
Vol 9 ◽  
Author(s):  
Lei Kou ◽  
Xiao-dong Gong ◽  
Yi Zheng ◽  
Xiu-hui Ni ◽  
Yang Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Texture Feature ◽  
Texture Features ◽  
Current Sensor ◽  
Stable Operation ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Three Phase ◽  
Feature Based

Three-phase PWM voltage-source rectifier (VSR) systems have been widely used in various energy conversion systems, where current sensors are the key component for state monitoring and system control. The current sensor faults may bring hidden danger or damage to the whole system; therefore, this paper proposed a random forest (RF) and current fault texture feature–based method for current sensor fault diagnosis in three-phase PWM VSR systems. First, the three-phase alternating currents (ACs) of the three-phase PWM VSR are collected to extract the current fault texture features, and no additional hardware sensors are needed to avoid causing additional unstable factors. Then, the current fault texture features are adopted to train the random forest current sensor fault detection and diagnosis (CSFDD) classifier, which is a data-driven CSFDD classifier. Finally, the effectiveness of the proposed method is verified by simulation experiments. The result shows that the current sensor faults can be detected and located successfully and that it can effectively provide fault locations for maintenance personnel to keep the stable operation of the whole system.

Personal Authentication through Finger Knuckle Geometric and Texture Feature Measurements

2017 ◽  
pp. 249-273
Author(s):  
Usha Kazhagamani ◽  
M. Ezhilarasan
Keyword(s):  
Proximal Phalanx ◽  
Geometric Analysis ◽  
Texture Feature ◽  
Texture Features ◽  
Distal Phalanx ◽  
Novel Approach ◽  
Identification Approach ◽  
Feature Information ◽  
Real Time Applications ◽  
Improved Performance

Finger Knuckle biometric is an emerging automated human identification approach that has received extensive significance in the area of research and real time applications in the recent past. Generally, a typical finger knuckle biometric system investigates the finger knuckle patterns present in the outer bend surface of the finger back region i.e., proximal phalanx region. In contrast, this paper focuses on the entire finger back region which includes proximal and distal phalanx of the finger knuckle surface for recognition. Further, this paper investigates a novel approach to achieve improved performance by simultaneous extraction and integration of finger knuckle geometric and texture features from a captured finger knuckle region. The geometric measures are derived by means of angular geometric analysis method which extracts angular-based feature information for unique identification. Similarly, texture measures are derived through statistical-based texture analysis methods.

An Empirical Evaluation of Name Semantic Network for Face Annotation

2020 ◽  
Vol 13 (4) ◽  
pp. 557-571
Author(s):  
Kasthuri Anburajan ◽  
Suruliandi Andavar ◽  
Poongothai Elango
Keyword(s):  
Feature Extraction ◽  
Local Binary Pattern ◽  
Semantic Network ◽  
Recognition Rate ◽  
Empirical Evaluation ◽  
Texture Features ◽  
Extraction Methods ◽  
Local Order ◽  
Extraction Techniques ◽  
Deep Feature

Background: Face annotation is the naming procedure to assign the correct name of a person who has emerged on an image. Objective: The main objective of this paper was to compare and evaluate six feature extraction techniques for face annotation under real-time challenging images and to find the best suitable feature for face annotation. Method: From literature review, it has been observed that Name Semantic Network (NSN) outperforms other annotation methods for various unconditioned images as well as ambiguous tags. However, the NSN’s performance can differ with various feature extraction techniques. Hence, its success is influenced by the feature extraction techniques that are used. Therefore, in this work, the NSN’s performance is experimented and evaluated with various feature extraction methods such as the Discrete Cosine Transform Local Binary Pattern (DCT-LBP), Discrete Fourier Transform Local Binary Pattern (DFT-LBP), Local Patterns of Gradients (LPOG), Gist, Local Order-constrained Gradient Orientations (LOGO) and Convolutional Neural Networks (CNNs) deep features. Results: Different feature extraction approaches demonstrate variations in performance with respect to a range of difficulties in face annotation using the Yahoo, LFW and IMFDB databases. The experimental results show that the deep feature method can achieve better recognition rate other than texture features. It confronts several issues in the presentation of a face in an image and produces better results. Conclusion: It is concluded that the CNNs deep feature is the best feature extraction technique that offers enhanced performance for face annotation.

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 Gender Classification from Facial Images

2019 ◽  
Vol 9 (1) ◽  
pp. 6217-6223 ◽  
Keyword(s):  
Real Life ◽  
Gender Classification ◽  
Data Sets ◽  
Gender Recognition ◽  
Life Situation ◽  
Iterative Model ◽  
Face Images ◽  
The Face ◽  
Real Life Situation ◽  
Facial Images

Gender is one striking feature that human can deduce effortlessly when looking at a face. Here, we try to classify the gender (male or female) based on the face images. The first part of this paper presents a review of different methods/approaches used for gender recognition. We present a comparative analysis for gender recognition using PCA, 2dPCA and its variants. Finally, we develop an iterative model using 2dPCA which updates itself when new samples are encountered. This model is expected to be fruitful in real-life situation as it can learn when it comes across new test samples. We consider CFD, CUHK, ORL and Yale facial data-sets for our experiments.

scholarly journals Research on college gymnastics teaching model based on multimedia image and image texture feature analysis

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ying Wu ◽  
Jikun Liu
Keyword(s):  
Human Body ◽  
Rapid Development ◽  
Texture Feature ◽  
Texture Features ◽  
Dynamics Simulation ◽  
Image Texture ◽  
Whole Body ◽  
Database Modeling ◽  
Rigid Model ◽  
The Subject

AbstractWith the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.

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