scholarly journals Some Information Geometric Aspects of Cyber Security by Face Recognition

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 878
Author(s):  
C. T. J. Dodson ◽  
John Soldera ◽  
Jacob Scharcanski
Keyword(s):  
Face Recognition ◽  
Cyber Security ◽  
Probability Distributions ◽  
Geodesic Distance ◽  
Recognition Method ◽  
Frequency Distributions ◽  
Face Images ◽  
User Access ◽  
Multidimensional Methods ◽  
Joint Probabilities

Secure user access to devices and datasets is widely enabled by fingerprint or face recognition. Organization of the necessarily large secure digital object datasets, with objects having content that may consist of images, text, video or audio, involves efficient classification and feature retrieval processing. This usually will require multidimensional methods applicable to data that is represented through a family of probability distributions. Then information geometry is an appropriate context in which to provide for such analytic work, whether with maximum likelihood fitted distributions or empirical frequency distributions. The important provision is of a natural geometric measure structure on families of probability distributions by representing them as Riemannian manifolds. Then the distributions are points lying in this geometrical manifold, different features can be identified and dissimilarities computed, so that neighbourhoods of objects nearby a given example object can be constructed. This can reveal clustering and projections onto smaller eigen-subspaces which can make comparisons easier to interpret. Geodesic distances can be used as a natural dissimilarity metric applied over data described by probability distributions. Exploring this property, we propose a new face recognition method which scores dissimilarities between face images by multiplying geodesic distance approximations between 3-variate RGB Gaussians representative of colour face images, and also obtaining joint probabilities. The experimental results show that this new method is more successful in recognition rates than published comparative state-of-the-art methods.

scholarly journals Half-face based recognition using principal component analysis

2021 ◽  
Vol 22 (3) ◽  
pp. 1404
Author(s):  
Ahmed M. Alkababji ◽  
Sara Raed Abd
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Method ◽  
Face Images ◽  
The Face ◽  
Considerable Problem ◽  
Full Face ◽  
Pca Algorithm

<span lang="EN-US">Face recognition is a considerable problem in the field of image processing. It is used daily in various applications from personal cameras to forensic investigations. Most of the provides solutions proposed based on full-face images, are slow to compute and need more storage. In this paper, we propose an effective way to reduce the features and size of the database in the face recognition method and thus we get an increase in the speed of discrimination by using half of the face. Taking advantage of face symmetry, the first step is to divide the face image into two halves, then the left half is processed using the principal component analysis (PCA) algorithm, and the results are compared by using Euclidian distance to distinguish the person. The system was trained and tested on ORL database. It was found that the accuracy of the system reached up to 96%, and the database was minimized by 46% and the running time was decreased from 120 msec to 70 msec with a 41.6% reduction.</span>

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 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.

A Face Recognition Method on Mobile Terminals Based on Manifold Learning

2014 ◽  
Vol 610 ◽  
pp. 307-311
Author(s):  
Yong Gang Li ◽  
Rong Zhu ◽  
Cong Cong Zhang ◽  
Xun Wei Gong
Keyword(s):  
Face Recognition ◽  
Manifold Learning ◽  
Nearest Neighbor ◽  
Facial Feature ◽  
Mapping Method ◽  
Mobile Terminals ◽  
Recognition Method ◽  
Snake Model ◽  
Face Images ◽  
The Face

A face recognition method on mobile terminals based on manifold learning was proposed. Firstly, the modified Snake model was set in order to improve the accuracy and effectiveness of facial feature point labeling. Then, the partial mapping method was carried out to map the face images to a subspace for further analysis. Finally, the nearest neighbor classifier was enhanced to show the recognition results. The experimental results indicate that the performance of this method is excellent. It is boasts a higher accuracy rate and bigger robustness than the ordinary methods.

BIONIC FACE RECOGNITION USING GABOR TRANSFORMATION

2011 ◽  
Vol 25 (03) ◽  
pp. 391-402 ◽  
Author(s):  
GUANGHUI HE ◽  
YUANYAN TANG ◽  
BIN FANG ◽  
Patrick S. P. WANG
Keyword(s):  
Face Recognition ◽  
Dimensionality Reduction ◽  
Face Database ◽  
Recognition Method ◽  
Feature Vectors ◽  
Face Images ◽  
Gabor Features ◽  
Gabor Feature ◽  
Pca Algorithm

In this paper, we propose a bionic face recognition method based on Gabor feature. First, Gabor features are extracted from face images, followed by dimensionality reduction using 2DPCA algorithm, which serves as the feature vectors of the proposed method. Finally, the bionic classifier is trained for classification. The experiment on AR and PIE face database is reported to show the effectiveness of the proposed method and compare it with Gabor-2DPCA algorithm and Gabor-PCA algorithm.

scholarly journals Combined Classifier for Face Recognition using Legendre Moments

2012 ◽  
Vol 1 (2) ◽  
pp. 107-118 ◽  
Author(s):  
Sridhar Dasari ◽  
I.V. Murali Krishna
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Dimensionality Reduction ◽  
Linear Discriminant Analysis ◽  
Probabilistic Neural Network ◽  
Recognition Method ◽  
Linear Discriminant ◽  
Face Images ◽  
Legendre Moments

In this paper, a new combined Face Recognition method based on Legendre moments with Linear Discriminant Analysis and Probabilistic Neural Network is proposed. The Legendre moments are orthogonal and scale invariants hence they are suitable for representing the features of the face images. The proposed face recognition method consists of three steps, i) Feature extraction using Legendre moments ii) Dimensionality reduction using Linear Discrminant Analysis (LDA) and iii) classification using Probabilistic Neural Network (PNN). Linear Discriminant Analysis searches the directions for maximum discrimination of classes in addition to dimensionality reduction. Combination of Legendre moments and Linear Discriminant Analysis is used for improving the capability of Linear Discriminant Analysis when few samples of images are available. Probabilistic Neural network gives fast and accurate classification of face images. Evaluation was performed on two face data bases. First database of 400 face images from Olivetty Research Laboratories (ORL) face database, and the second database of thirteen students are taken. The proposed method gives fast and better recognition rate when compared to other classifiers.DOI: 10.18495/comengapp.12.107118

Local Comparative Decimal Pattern for Face Recognition

2020 ◽  
Vol 34 (12) ◽  
pp. 2056006 ◽  
Author(s):  
Mohsen Tabejamaat ◽  
Abdolmajid Mousavi ◽  
Marina L. Gavrilova
Keyword(s):  
Face Recognition ◽  
Training Sample ◽  
Elastic Model ◽  
Recognition Method ◽  
Texture Representation ◽  
Face Region ◽  
Face Images ◽  
The Face ◽  
Representation Technique ◽  
Query Sample

Rapid growth of social networks has provided an extraordinary medium to share a large volume of photographs online. This calls for designing efficient face recognition techniques that are applicable to images with low resolutions and arbitrary poses. This paper proposes a new pose invariant face recognition method for low resolution images using only a single training sample. A 3D model, reconstructed using Generic Elastic Model (3D GEM) from a frontal view training sample, is used to generate a set of nonfrontal gallery face images. The face region of the nonfrontal query sample is then extracted using the same landmark detection technique as in the 3D GEM algorithm. Afterwards, a novel texture representation technique called Local Comparative Decimal Pattern (LCDP) is proposed to extract features from each of the training and query samples. A set of experimental results on the ORL, Georgia Tech (GT), and LFW face databases demonstrates the efficiency of the proposed method compared to other state-of-the-art approaches.

scholarly journals Open Set Sheep Face Recognition Based on Euclidean Space Metric

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hongcheng Xue ◽  
Junping Qin ◽  
Chao Quan ◽  
Wei Ren ◽  
Tong Gao ◽  
...  
Keyword(s):  
Face Recognition ◽  
Animal Husbandry ◽  
Face Image ◽  
Recognition Method ◽  
Data Set ◽  
Identity Recognition ◽  
Identity Information ◽  
Face Area ◽  
Face Images ◽  
Open Set

As the essential content of intelligent animal husbandry, identifying each livestock is the only way to achieve modern and refined scientific husbandry. This paper proposes a sheep face recognition method based on European spatial metrics and realizes noncontact sheep identity recognition by training the network using sheep face image samples in the natural environment. The SheepBase data set was first proposed in this process, which contains 6559 images of Inner Mongolia fine-wool sheep and Sunite sheep. To enhance the diversity of the data, the sheep face images were data-enhanced. Secondly, to solve the problems of more redundant information in the sheep face image and the poor posture and angle of the sheep face, we propose the sheep face detection and correction (SheepFaceRepair) method. This method aims to detect the sheep face area in the image to be recognized and align the sheep face area. On this basis, we offer an open sheep facial recognition network (SheepFaceNet) based on the European spatial metric. This method incorporates the biological identity information features of the sheep face to achieve sheep identity. We also tested the effectiveness of this method in the SheepBase data set. The experimental results show that the method proposed in this paper is much higher than the other methods, and the precision of recognition reaches 89.12%. In addition, we found that integrating the biometrics of the sheep face can effectively improve the network’s recognition capacity.

Convolutional Approach Also Benefits Traditional Face Pattern Recognition Algorithm [208!]

2019 ◽  
Vol 11 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Yunke Li ◽  
Hongyuan Shi ◽  
Liang Chen ◽  
Fan Jiang
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Recognition Algorithm ◽  
Mathematical Approach ◽  
Recognition Method ◽  
Pattern Recognition Algorithm ◽  
Face Images ◽  
Comparison Approach ◽  
Learning Frameworks ◽  
Face Pattern

Convolutional neural networks (CNN) are widely used deep learning frameworks and are applied in the field of face recognition, achieving outstanding results. The Macropixel comparison approach is a shallow mathematical approach that recognizes faces by comparing the original pixel blocks of face images. In this article, the authors are inspired by ideas of the currently popular deep neural network framework and introduce two features into the mathematical approach: deep overlap and weighted filter. The aim is to explore if the idea of deep learning could benefit mathematical recognition method, which might extend the scope of face recognition research. Results from the experiments show that the new proposed approach achieves markedly better recognition rates than the original macropixel methods.

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