scholarly journals An Analysis of Face Recognition under Face Mask Occlusions

2021 ◽  
Author(s):  
Susith Hemathilaka ◽  
Achala Aponso
Keyword(s):  
Face Recognition ◽  
Rapid Development ◽  
Face Mask ◽  
Large Area ◽  
Daily Lives ◽  
Lighting Conditions ◽  
Intractable Problems ◽  
The Face ◽  
Recognition Systems ◽  
Pose Variation

The face mask is an essential sanitaryware in daily lives growing during the pandemic period and is a big threat to current face recognition systems. The masks destroy a lot of details in a large area of face and it makes it difficult to recognize them even for humans. The evaluation report shows the difficulty well when recognizing masked faces. Rapid development and breakthrough of deep learning in the recent past have witnessed most promising results from face recognition algorithms. But they fail to perform far from satisfactory levels in the unconstrained environment during the challenges such as varying lighting conditions, low resolution, facial expressions, pose variation and occlusions. Facial occlusions are considered one of the most intractable problems. Especially when the occlusion occupies a large region of the face because it destroys lots of official features.

scholarly journals Implementing CCTV-Based Attendance Taking Support System Using Deep Face Recognition: A Case Study at FPT Polytechnic College

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 307 ◽  
Author(s):  
Ngo Tung Son ◽  
Bui Ngoc Anh ◽  
Tran Quy Ban ◽  
Le Phuong Chi ◽  
Bui Dinh Chien ◽  
...  
Keyword(s):  
Face Recognition ◽  
Motion Blur ◽  
Security Monitoring ◽  
Flexible System ◽  
Lighting Conditions ◽  
Satisfactory Performance ◽  
Measurement Results ◽  
The Face ◽  
Recognition Systems

Face recognition (FR) has received considerable attention in the field of security, especially in the use of closed-circuit television (CCTV) cameras in security monitoring. Although significant advances in the field of computer vision are made, advanced face recognition systems provide satisfactory performance only in controlled conditions. They deteriorate significantly in the face of real-world scenarios such as lighting conditions, motion blur, camera resolution, etc. This article shows how we design, implement, and conduct the empirical comparisons of machine learning open libraries in building attendance taking (AT) support systems using indoor security cameras called ATSS. Our trial system was deployed to record the appearances of 120 students in five classes who study on the third floor of FPT Polytechnic College building. Our design allows for flexible system scaling, and it is not only usable for a school but a generic attendance system with CCTV. The measurement results show that the accuracy is suitable for many different environments.

Local Linear Regression on Hybrid Eigenfaces for Pose Invariant Face Recognition

2012 ◽  
Vol 2 (2) ◽  
pp. 48-58 ◽  
Author(s):  
Ajay Jaiswal ◽  
Nitin Kumar ◽  
R. K. Agrawal
Keyword(s):  
Face Recognition ◽  
Linear Regression ◽  
Classification Accuracy ◽  
Local Linear Regression ◽  
New Approach ◽  
Local Linear ◽  
The Face ◽  
Recognition Systems ◽  
Pose Variation ◽  
Gallery Image

Pose variation leads to significant decline in the performance of the face recognition systems. In this paper, the authors propose a new approach HLLR, based on conjunction of hybrid-eigenfaces and local linear regression (LLR), to perform face recognition across pose. In this approach, LLR on hybrid-eigenfaces is used to generate virtual views. These virtual views in frontal and non-frontal poses are obtained using frontal gallery image. The performance of the proposed approach is compared for classification accuracy with another efficient method based on global linear regression on hybrid eigenface (HGLR). They also investigate the effect of number of images used to construct hybrid-eigenfaces on classification accuracy. Experimental results on two well known publicly available face databases demonstrate the effectiveness of the proposed approach. The suitability of proposed approach is also noticed when the number of available images is small.

scholarly journals Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic

2021 ◽  
Vol 13 (12) ◽  
pp. 6900
Author(s):  
Jonathan S. Talahua ◽  
Jorge Buele ◽  
P. Calvopiña ◽  
José Varela-Aldás
Keyword(s):  
Facial Recognition ◽  
Face Mask ◽  
Recognition System ◽  
Classification Model ◽  
World Health ◽  
The Face ◽  
Recognition Systems ◽  
Facial Recognition System ◽  
Health Organization ◽  
Face Detector

In the face of the COVID-19 pandemic, the World Health Organization (WHO) declared the use of a face mask as a mandatory biosafety measure. This has caused problems in current facial recognition systems, motivating the development of this research. This manuscript describes the development of a system for recognizing people, even when they are using a face mask, from photographs. A classification model based on the MobileNetV2 architecture and the OpenCv’s face detector is used. Thus, using these stages, it can be identified where the face is and it can be determined whether or not it is wearing a face mask. The FaceNet model is used as a feature extractor and a feedforward multilayer perceptron to perform facial recognition. For training the facial recognition models, a set of observations made up of 13,359 images is generated; 52.9% images with a face mask and 47.1% images without a face mask. The experimental results show that there is an accuracy of 99.65% in determining whether a person is wearing a mask or not. An accuracy of 99.52% is achieved in the facial recognition of 10 people with masks, while for facial recognition without masks, an accuracy of 99.96% is obtained.

Performance Analysis of Pose Invariant Face Recognition Approaches in Unconstrained Environments

2015 ◽  
Vol 5 (1) ◽  
pp. 66-81 ◽  
Author(s):  
M. Parisa Beham ◽  
S. M. Mansoor Roomi ◽  
J. Alageshan ◽  
V. Kapileshwaran
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
State Of The Art ◽  
Recognition Algorithm ◽  
K Nearest Neighbor ◽  
Research Issues ◽  
Histograms Of Oriented Gradients ◽  
Recognition Phase ◽  
The Face ◽  
Pose Variation

Face recognition and authentication are two significant and dynamic research issues in computer vision applications. There are many factors that should be accounted for face recognition; among them pose variation is a major challenge which severely influence in the performance of face recognition. In order to improve the performance, several research methods have been developed to perform the face recognition process with pose invariant conditions in constrained and unconstrained environments. In this paper, the authors analyzed the performance of a popular texture descriptors viz., Local Binary Pattern, Local Derivative Pattern and Histograms of Oriented Gradients for pose invariant problem. State of the art preprocessing techniques such as Discrete Cosine Transform, Difference of Gaussian, Multi Scale Retinex and Gradient face have also been applied before feature extraction. In the recognition phase K- nearest neighbor classifier is used to accomplish the classification task. To evaluate the efficiency of pose invariant face recognition algorithm three publicly available databases viz. UMIST, ORL and LFW datasets have been used. The above said databases have very wide pose variations and it is proved that the state of the art method is efficient only in constrained situations.

scholarly journals DTFA-Net: Dynamic and Texture Features Fusion Attention Network for Face Antispoofing

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xin Cheng ◽  
Hongfei Wang ◽  
Jingmei Zhou ◽  
Hui Chang ◽  
Xiangmo Zhao ◽  
...  
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Feature Fusion ◽  
Texture Features ◽  
Replay Attack ◽  
Attention Network ◽  
Detection Failure ◽  
The Face ◽  
Recognition Systems ◽  
Common Face

For face recognition systems, liveness detection can effectively avoid illegal fraud and improve the safety of face recognition systems. Common face attacks include photo printing and video replay attacks. This paper studied the differences between photos, videos, and real faces in static texture and motion information and proposed a living detection structure based on feature fusion and attention mechanism, Dynamic and Texture Fusion Attention Network (DTFA-Net). We proposed a dynamic information fusion structure of an interchannel attention block to fuse the magnitude and direction of optical flow to extract facial motion features. In addition, for the face detection failure of HOG algorithm under complex illumination, we proposed an improved Gamma image preprocessing algorithm, which effectively improved the face detection ability. We conducted experiments on the CASIA-MFSD and Replay Attack Databases. According to experiments, the DTFA-Net proposed in this paper achieved 6.9% EER on CASIA and 2.2% HTER on Replay Attack that was comparable to other methods.

Face Recognition Using Completed Local Ternary Pattern (CLTP) Texture Descriptor

2017 ◽  
Vol 7 (3) ◽  
pp. 1594 ◽  
Author(s):  
Taha H. Rassem ◽  
Nasrin M. Makbol ◽  
Sam Yin Yee
Keyword(s):  
Face Recognition ◽  
Recognition Task ◽  
Texture Features ◽  
Classification Rate ◽  
Local Ternary Pattern ◽  
Japanese Female ◽  
The Face ◽  
Recognition Systems ◽  
High Recognition Accuracy ◽  
Event Image

Nowadays, face recognition becomes one of the important topics in the computer vision and image processing area. This is due to its importance where can be used in many applications. The main key in the face recognition is how to extract distinguishable features from the image to perform high recognition accuracy.  Local binary pattern (LBP) and many of its variants used as texture features in many of face recognition systems. Although LBP performed well in many fields, it is sensitive to noise, and different patterns of LBP may classify into the same class that reduces its discriminating property. Completed Local Ternary Pattern (CLTP) is one of the new proposed texture features to overcome the drawbacks of the LBP. The CLTP outperformed LBP and some of its variants in many fields such as texture, scene, and event image classification.  In this study, we study and investigate the performance of CLTP operator for face recognition task. The Japanese Female Facial Expression (JAFFE), and FEI face databases are used in the experiments. In the experimental results, CLTP outperformed some previous texture descriptors and achieves higher classification rate for face recognition task which has reached up 99.38% and 85.22% in JAFFE and FEI, respectively.

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 Realtime Face-Mask Detection on Raspberry Kit

2021 ◽  
Vol 9 (8) ◽  
pp. 2076-2083
Author(s):  
Kalyan Chakravarthi. M
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Real Life ◽  
Face Mask ◽  
Recognition System ◽  
Primary Concern ◽  
Public Places ◽  
Life Problems ◽  
Promising Area ◽  
The Face

Abstract: Recognition from faces is a popular and significant technology in recent years. Face alterations and the presence of different masks make it too much challenging. In the real-world, when a person is uncooperative with the systems such as in video surveillance then masking is further common scenarios. For these masks, current face recognition performance degrades. Still, difficulties created by masks are usually disregarded. Face recognition is a promising area of applied computer vision . This technique is used to recognize a face or identify a person automatically from given images. In our daily life activates like, in a passport checking, smart door, access control, voter verification, criminal investigation, and many other purposes face recognition is widely used to authenticate a person correctly and automatically. Face recognition has gained much attention as a unique, reliable biometric recognition technology that makes it most popular than any other biometric technique likes password, pin, fingerprint, etc. Many of the governments across the world also interested in the face recognition system to secure public places such as parks, airports, bus stations, and railway stations, etc. Face recognition is one of the well-studied real-life problems. Excellent progress has been done against face recognition technology throughout the last years. The primary concern to this work is about facial masks, and especially to enhance the recognition accuracy of different masked faces. A feasible approach has been proposed that consists of first detecting the facial regions. The occluded face detection problem has been approached using Cascaded Convolutional Neural Network (CNN). Besides, its performance has been also evaluated within excessive facial masks and found attractive outcomes. Finally, a correlative study also made here for a better understanding.

scholarly journals A face recognition system using convolutional feature extraction with linear collaborative discriminant regression classification

2022 ◽  
Vol 12 (2) ◽  
pp. 1468
Author(s):  
Sangamesh Hosgurmath ◽  
Viswanatha Vanjre Mallappa ◽  
Nagaraj B. Patil ◽  
Vishwanath Petli
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Distance Ratio ◽  
Research Areas ◽  
Lighting Conditions ◽  
Learning Techniques ◽  
Face Recognition System ◽  
The Face

Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in convolutional neural network (CNN) and also used to make the model robust than other traditional feature extraction technique called local multiple pattern (LMP). The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the features inside the class reduces. The results stated that the CNN-LCDRC achieved 93.10% and 87.60% of mean recognition accuracy, where traditional LCDRC achieved 83.35% and 77.70% of mean recognition accuracy on ORL and YALE databases respectively for the training number 8 (i.e. 80% of training and 20% of testing data).

Age-Invariant Face Recognition Using Triangle Geometric Features

2015 ◽  
Vol 29 (05) ◽  
pp. 1556006 ◽  
Author(s):  
Amal Seralkhatem Osman Ali ◽  
Vijanth Sagayan Asirvadam ◽  
Aamir Saeed Malik ◽  
Mohamed Meselhy Eltoukhy ◽  
Azrina Aziz
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Facial Recognition ◽  
Research Network ◽  
Facial Features ◽  
Age Difference ◽  
Facial Aging ◽  
The Face ◽  
Recognition Systems ◽  
Mathematical Relationships

Whilst facial recognition systems are vulnerable to different acquisition conditions, most notably lighting effects and pose variations, their particular level of sensitivity to facial aging effects is yet to be researched. The face recognition vendor test (FRVT) 2012's annual statement estimated deterioration in the performance of face recognition systems due to facial aging. There was about 5% degradation in the accuracies of the face recognition systems for each single year age difference between a test image and a probe image. Consequently, developing an age-invariant platform continues to be a significant requirement for building an effective facial recognition system. The main objective of this work is to address the challenge of facial aging which affects the performance of facial recognition systems. Accordingly, this work presents a geometrical model that is based on extracting a number of triangular facial features. The proposed model comprises a total of six triangular areas connecting and surrounding the main facial features (i.e. eyes, nose and mouth). Furthermore, a set of thirty mathematical relationships are developed and used for building a feature vector for each sample image. The areas and perimeters of the extracted triangular areas are calculated and used as inputs for the developed mathematical relationships. The performance of the system is evaluated over the publicly available face and gesture recognition research network (FG-NET) face aging database. The performance of the system is compared with that of some of the state-of-the-art face recognition methods and state-of-the-art age-invariant face recognition systems. Our proposed system yielded a good performance in term of classification accuracy of more than 94%.

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