scholarly journals Face Detection & Recognition using Tensor Flow: A Review

2018 ◽  
Vol 18 ◽  
pp. 7381-7388
Author(s):  
Ishaan Chawla
Keyword(s):  
Social Media ◽  
Face Recognition ◽  
Access Control ◽  
Face Detection ◽  
Facial Recognition ◽  
Rapid Development ◽  
Standard Form ◽  
Surveillance Systems ◽  
Security Systems ◽  
Recognition Systems

Face recognition has become a popular topic of research recently due to increases in demand for security as well as the rapid development of mobile devices. There are many applications which face recognition can be applied to such as access control, identity verification, security systems, surveillance systems, and social media networks. Access control includes offices, computers, phones, ATMs, etc. Most of these forms currently do not use face recognition as the standard form of granting entry, but with advancing technologies in computers along with more refined algorithms, facial recognition is gaining some traction in replacing passwords and fingerprint scanners. Ever since the events of 9/11 there has been a more concerned emphasis on developing security systems to ensure the safety of innocent citizens. Namely in places such as airports and border crossings where identification verification is necessary, face recognition systems potentially have the ability to mitigate the risk and ultimately prevent future attacks from occurring. As for surveillance systems, the same point can be made if there are criminals on the loose. Surveillance cameras with face recognition abilities can aide in efforts of finding these individuals. Alternatively, these same surveillance systems can also help identify the whereabouts of missing persons, although this is dependent on robust facial recognition algorithms as well as a fully developed database of faces. And lastly, facial recognition has surfaced in social media applications on platforms such as Facebook which suggest users to tag friends who have been identified in pictures. It is clear that there are many applications the uses for facial recognition systems. In general, the steps to achieve this are the following: face detection, feature extraction, and lastly training a model.

Application of 3D face recognition in the access control system

Robotica ◽  
2021 ◽  
pp. 1-19
Author(s):  
Quoc Dien Le ◽  
Tran Thanh Cong Vu ◽  
Tuong Quan Vo
Keyword(s):  
Control System ◽  
Face Recognition ◽  
Access Control ◽  
Facial Recognition ◽  
Recognition Algorithm ◽  
User Cooperation ◽  
Web Based ◽  
Access Control System ◽  
Recognition Systems ◽  
And Storage

Abstract Over the years, face recognition has been the research topic that has attracted many researchers around the world. One of the most significant applications of face recognition is the access control system. The access control system allows authorized persons to enter or exit certain or restricted areas. As a result, it will increase the security situation without over-investment in staff security. The access information can be the identification, time, and location, etc. It can be used to carry out human resource management tasks such as attendance and inspection of employees in a more fair and transparent manner. Although face recognition has been widely used in access control systems because of its better accuracy and convenience without requiring too much user cooperation, the 2D-based face recognition systems also retain many limitations due to the variations in pose and illumination. By analyzing facial geometries, 3D facial recognition systems can theoretically overcome the disadvantages of prior 2D methods and improve robustness in different working conditions. In this paper, we propose the 3D facial recognition algorithm for use in an access control system. The proposed algorithm includes the preprocessing, feature extraction, and classification stages. The application of the proposed access control system is the automatic sliding door, the controller of the system, the web-based monitoring, control, and storage of data.

A Few Approaches To Face Detection In Face Recognition Systems

2002 ◽  
pp. 313-322
Author(s):  
Georgi Koukharev ◽  
Tomasz Ponikowski ◽  
Liming Chen
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Recognition Systems

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.

scholarly journals Deep Learning- Based Surveillance System using Face Recognition

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Learning Method ◽  
Security Systems ◽  
The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

scholarly journals Face Recognition Based on Statistical Texture Features

2021 ◽  
Vol 7 (1) ◽  
pp. 10-15
Author(s):  
Lama Akram Ibrahim ◽  
Nasser Nasser ◽  
Majd Ali
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Face Recognition ◽  
Processing Time ◽  
Facial Recognition ◽  
Texture Features ◽  
Memory Consumption ◽  
Methods And Techniques ◽  
Recognition Systems ◽  
Occurrence Matrix

Facial recognition has attracted the attention of researchers and has been one of the most prominent topics in the fields of image processing and pattern recognition since 1990. This resulted in a very large number of recognition methods and techniques with the aim of increasing the accuracy and robustness of existing systems. Many techniques have been developed to address the challenges and reliable recognition systems have been reached but require considerable processing time, suffer from high memory consumption and are relatively complex. The focus of this paper is on extracting subset of descriptors (less correlated and less calculations) from the co-occurrence matrix with the goal of enhancing the performance of Haralick’s descriptors. Improvements are achieved by adding the image pre-processing and selecting the proper method according to the database problem and by extracting features from image local regions.

scholarly journals Facial Recognition on System Prototype to Verify Users using Eigenface, Viola-Jones and Haar

2021 ◽  
Vol 3 (2) ◽  
pp. 213-222
Author(s):  
Robin Robin ◽  
Aldrick Handinata ◽  
Wenripin Chandra
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Facial Recognition ◽  
Prototype System ◽  
Body Parts ◽  
Test Results ◽  
Haar Feature ◽  
System Prototype

Facial recognition is one of the most popular way to authenticate user into a system. This method is preferable considering the tendency of users for using the same password across multiple sites which made the user has already made his own account securities in vulnerable states. Using biometrics might supply solutions to solve this problem and facial recognition is one of the best biometric methods can be apply as a digital account security solution. This study to design a prototype system implementing facial recognition to verify users to measure how accurate these methods are. The method used here is Viola-Jones for face detection, Eigenface and Haar feature for face recognition from the OpenCV. The system was designed in Java. Based on the test results from the system designed, system can recognize user face with 100% accuracy if faces are shot in a well desirable condition. The system is able to recognize the user's face with various expressions including with or without glasses. However, the system has difficulty in recognizing user’s face in facing up, down, sideways position or blocked by accessories or body parts such as hands. After some experiment, it was proven that the system designed is accurate, reliable and safe enough to be implemented to digital authorization process.

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.

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%.

Similarity Measures for Face Recognition

2015 ◽  
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Facial Recognition ◽  
Similarity Measures ◽  
Corrective Surgery ◽  
Mahalanobis Distances ◽  
Computer Scientists ◽  
Criminal Suspects ◽  
Recognition Systems ◽  
Minkowski Distances

Similarity Measures for Face Recognition Face recognition has several applications, including security, such as (authentication and identification of device users and criminal suspects), and in medicine (corrective surgery and diagnosis). Facial recognition programs rely on algorithms that can compare and compute the similarity between two sets of images. This eBook explains some of the similarity measures used in facial recognition systems in a single volume. Readers will learn about various measures including Minkowski distances, Mahalanobis distances, Hansdorff distances, cosine-based distances, among other methods. The book also summarizes errors that may occur in face recognition methods. Computer scientists "facing face" and looking to select and test different methods of computing similarities will benefit from this book. The book is also useful tool for students undertaking computer vision courses.

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