scholarly journals Citizen Unique ID Based Real Time Face Recognition for Surveillance and National Security

2020 ◽  
Vol 8 (6) ◽  
pp. 1830-1834
Keyword(s):  
Face Recognition ◽  
National Security ◽  
Motion Detection ◽  
Principal Component ◽  
Single System ◽  
Security Services ◽  
Key Innovation ◽  
The Face ◽  
Adaptive Principal Component Analysis ◽  
Better Than

Provision of home security services has become an integral part of our lives in today’s technological society where attackers usually have all the necessary means and resources at their disposal. Face Recognition is producing gigantic enthusiasm because of government worries about character the executives and worldwide fear based oppressor movement. One aspiration of Intelligent CCTV is to help counteract fear based oppression and a key innovation is solid face acknowledgment. The movement discovery module is dependable to decide the degree of action while the face identification module separates between approved individuals and interlopers. Our system is better than many proposed systems as it combines both motion detection and face recognition in a single system. Our framework has three noteworthy segments containing: 1) a Viola-Jones face discovery module 2) a Pose Normalization Module to evaluate facial posture and make up for extraordinary posture points 3) Adaptive Principal Component Analysis to perceive the standardized appearances.

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

Adaptive GLOH with PSO-trained NN for the recognition of plastic surgery faces and their types

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Archana Harsing Sable ◽  
Sanjay N. Talbar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Plastic Surgery ◽  
Local Binary Pattern ◽  
Principal Component ◽  
Traditional Learning ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Orientation Histogram ◽  
The Face

Abstract Numerous algorithms have met complexity in recognizing the face, which is invariant to plastic surgery, owing to the texture variations in the skin. Though plastic surgery serves to be a challenging issue in the domain of face recognition, the concerned theme has to be restudied for its hypothetical and experimental perspectives. In this paper, Adaptive Gradient Location and Orientation Histogram (AGLOH)-based feature extraction is proposed to accomplish effective plastic surgery face recognition. The proposed features are extracted from the granular space of the faces. Additionally, the variants of the local binary pattern are also extracted to accompany the AGLOH features. Subsequently, the feature dimensionality is reduced using principal component analysis (PCA) to train the artificial neural network. The paper trains the neural network using particle swarm optimization, despite utilizing the traditional learning algorithms. The experimentation involved 452 plastic surgery faces from blepharoplasty, brow lift, liposhaving, malar augmentation, mentoplasty, otoplasty, rhinoplasty, rhytidectomy and skin peeling. Finally, the proposed AGLOH proves its performance dominance.

Application of Gabor Dictionary and SL0 Norm in Disguise Face Recognition

2013 ◽  
Vol 278-280 ◽  
pp. 1211-1214
Author(s):  
Jun Ying Zeng ◽  
Jun Ying Gan ◽  
Yi Kui Zhai
Keyword(s):  
Face Recognition ◽  
Function Approximation ◽  
Multiple Scales ◽  
Recognition Rate ◽  
Principal Component ◽  
Facial Feature ◽  
Training Image ◽  
Recognition Algorithm ◽  
The Face ◽  
Gabor Dictionary

A fast sparse representation face recognition algorithm based on Gabor dictionary and SL0 norm is proposed in this paper. The Gabor filters, which could effectively extract local directional features of the image at multiple scales, are less sensitive to variations of illumination, expression and camouflage. SL0 algorithm, with the advantages of calculation speed，require fewer measurement values by continuously differentiable function approximation L0 norm and reconstructed sparse signal by minimizing the approximate L0 norm. The algorithm obtain the local feature face by extracting the Gabor face feature, reduce the dimensions by principal component analysis, fast sparse classify by the SL0 norm. Under camouflage condition, The algorithm block the Gabor facial feature and improve the speed of formation of the Gabor dictionary. The experimental results on AR face database show that the proposed algorithm can improve recognition speed and recognition rate to some extent and can generalize well to the face recognition, even with a few training image per class.

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 ILLUMINATION PROCESSING IN FACE RECOGNITION

2014 ◽  
Vol 28 (05) ◽  
pp. 1456011 ◽  
Author(s):  
ZHENXUE CHEN ◽  
CHENGYUN LIU ◽  
FALIANG CHANG ◽  
XUZHEN HAN ◽  
KAIFANG WANG
Keyword(s):  
Adverse Effect ◽  
Face Recognition ◽  
Light Intensity ◽  
Negative Impact ◽  
Processing Method ◽  
Dark Region ◽  
Light Region ◽  
The Face ◽  
Recognition Systems ◽  
Better Than

Changes in light intensity and angle present a major challenge to the creation of reliable face recognition systems. The existence of bright regions and dark regions has been shown to have a serious negative impact on the performance of face recognition systems. This paper proposes a solution to this problem based on self-quotient image (SQI) processing method. In this method, bright and dark areas are processed separately without changing the essential characteristics of the image of the face. The dark and light areas are processed separately by SQI. Experimental results indicate that this Single-Light-Region and Single-Dark-Region SQI method removes the adverse effect of multi-bright and multi-dark areas better than competing methods.

scholarly journals Analysis of Age Invariant Face Recognition using QSVM-PCA

2020 ◽  
Author(s):  
ASHUTOSH DHAMIJA ◽  
R.B DUBEY
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Support Vector ◽  
Facial Features ◽  
Identity Recognition ◽  
The Face ◽  
A New Technique ◽  
Age Range ◽  
Face Aging

Abstract Forage, face recognition is one of the most demanding field challenges, since aging affects the shape and structure of the face. Age invariant face recognition (AIFR) is a relatively new area in face recognition studies, which in real-world implementations recently gained considerable interest due to its huge potential and relevance. The AIFR, however, is still evolving and evolving, providing substantial potential for further study and progress inaccuracy. Major issues with the AIFR involve major variations in appearance, texture, and facial features and discrepancies in position and illumination. These problems restrict the AIFR systems developed and intensify identity recognition tasks. To address this problem, a new technique Quadratic Support Vector Machine- Principal Component Analysis (QSVM-PCA) is introduced. Experimental results suggest that our QSVM-PCA achieved better results especially when the age range is larger than other existing techniques of face-aging datasets of FGNET. The maximum accuracy achieved by demonstrated methodology is 98.87%.

A Review on Biometric Technology

2018 ◽  
Vol 8 (4) ◽  
pp. 101
Author(s):  
Kartik Choudhary ◽  
Rizwan Khan
Keyword(s):  
Face Recognition ◽  
Iris Recognition ◽  
Review Paper ◽  
Behavioral Characteristics ◽  
Biometric System ◽  
Biometric Technology ◽  
The Face ◽  
The Individual ◽  
Recognition Technique ◽  
Better Than

Biometric Technology has turned out to be a popular area of research in computer vision and one of the most successful applications for identifying humans by capturing and analysing the sole feature or characteristic of   individual which is possessed by them and involves their Physical and Behavioral characteristics. For the individual validation and authentication the biometric system has this responsibility. Biometric Technology started from the fingerprints recognition and later on improvements were done in it to make it more secure which involves the face recognition and iris Recognition. Almost both of them are available and regarded as the accurate and reliable technology for biometric validation system. This review paper is all about Face recognition techniques in biometric locking system and Iris recognition technique of identification and the ways of making locking systems ways more efficient, full of ease, more secure, and far better than before so as to make locking or security stronger. It discusses about face recognition technique, its working and its application in different sector along with iris recognition, its working, its application.

Robust Face Recognition Technique for a Real-Time Embedded Face Recognition System

2008 ◽  
pp. 188-211
Author(s):  
Ting Shan ◽  
Abbas Bigdeli ◽  
Brian C. Lovell ◽  
Shaokang Chen
Keyword(s):  
Face Recognition ◽  
Embedded System ◽  
Principal Component ◽  
Recognition System ◽  
Active Appearance Models ◽  
Face Recognition System ◽  
The Face ◽  
The Embedded System ◽  
Computationally Intensive ◽  
Robust Face Recognition

In this chapter, we propose a pose variability compensation technique, which synthesizes realistic frontal face images from nonfrontal views. It is based on modeling the face via active appearance models and estimating the pose through a correlation model. The proposed technique is coupled with adaptive principal component analysis (APCA), which was previously shown to perform well in the presence of both lighting and expression variations. The proposed recognition techniques, though advanced, are not computationally intensive. So they are quite well suited to the embedded system environment. Indeed, the authors have implemented an early prototype of a face recognition module on a mobile camera phone so the camera can be used to identify the person holding the phone.

scholarly journals Diagnosis of Esophagitis Based on Face Recognition Techniques

2010 ◽  
Vol 4 (1) ◽  
pp. 58-62
Author(s):  
Santosh S Saraf ◽  
Gururaj R Udupi ◽  
Santosh D Hajare
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Techniques ◽  
The Face ◽  
Pca Method ◽  
Recognition Efficiency ◽  
Motivating Factor

Face recognition technology has evolved over years with the Principal Component Analysis (PCA) method being the benchmark for recognition efficiency. The face recognition techniques take care of variation of illumination, pose and other features of the face in the image. We envisage an application of these face recognition techniques for classification of medical images. The motivating factor being, given a condition of an organ it is represented by some typical features. In this paper we report the use of the face recognition techniques to classify the type of Esophagitis, a condition of inflammation of the esophagus. The image of the esophagus is captured in the process of endoscopy. We test PCA, Fisher Face method and Independent Component Analysis techniques to classify the images of the esophagus. Esophagitis is classified into four categories. The results of classification for each method are reported and the results are compared.

Makeup Invariant Face Recognition using Features from Accelerated Segment Test and Eigen Vectors

2017 ◽  
Vol 17 (01) ◽  
pp. 1750005 ◽  
Author(s):  
Aruna Bhat
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Training Data ◽  
Data Sets ◽  
Security Breaches ◽  
Daunting Task ◽  
Eigen Values ◽  
The Face ◽  
Robust Face Recognition

A methodology for makeup invariant robust face recognition based on features from accelerated segment test and Eigen vectors is proposed. Makeup and cosmetic changes in face have been a major cause of security breaches since long time. It is not only difficult for human eyes to catch an imposter but also an equally daunting task for a face recognition system to correctly identify an individual owing to changes brought about in face due to makeup. As a crucial pre-processing step, the face is first divided into various segments centered on the eyes, nose, lips and cheeks. FAST algorithm is then applied over the face images. The features thus derived from the facial image act as the fiducial points for that face. Thereafter principal component analysis is applied over the set of fiducial points in each segment of every face image present in the data sets in order to compute the Eigen vectors and the Eigen values. The resultant principal component which is the Eigen vector with the highest Eigen value yields the direction of the features in that segment. The principal components thus obtained using fiducial points generated from FAST in each segment of the test and the training data are compared in order to get the best match or no match.

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