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 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.

Fast Face Recognition on GPUs

2014 ◽  
Vol 905 ◽  
pp. 543-547
Author(s):  
Yi Lei ◽  
Xiao Ya Fan ◽  
Meng Zhang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Face Recognition ◽  
Processing Speed ◽  
Principal Component ◽  
Great Accuracy ◽  
Analysis Algorithm ◽  
Training Images ◽  
Principal Component Analysis Algorithm

Face recognition is popular in the field of pattern recognition and image processing. However, traditional recognition technologies spend too long there are a lot of images to be recognized or trained for great accuracy in the recognition. Parallel computing is an effective way to improve the processing speed. With the improvement of GPU performance, its widely applied in computing-concentrated data operations. This paper presents a study of performance speedup achieved by applying GPU for face recognition based on PCA (Principal Component Analysis) algorithm. We successfully accelerated the testing phase by 6868-folds compared to a sequential C implementation when it has 100 test images and 2400 training images.

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 Attendance Management System using Google Facenet for Facial Recognition

2021 ◽  
Vol 9 (VI) ◽  
pp. 5029-5036
Author(s):  
Rakesh Duggempudi
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Open Source ◽  
Management System ◽  
Facial Recognition ◽  
Cost Effective ◽  
Source Image ◽  
Student Attendance ◽  
Manual Work ◽  
View Update

Attendance management system is a required tool for attaining attendance in any habitat where attendance is essential. Yet, many of the available techniques consume time, are invasive and it demands manual work from the users. This research is directed at building a less invasive, cost effective and more efficient automated student attendance management system using face recognition that leverages on OpenCV functions for facial recognition. The system provides a GUI for marking attendance. It provides an interface for updating attendance using facial recognition libraries of OpenCV. The system stores attendance in a database which is maintained by the administrator. The administrator can view, update, and change the attendance of the students. The students can view and update their attendance. The system is developed on Open-Source image processing library and the interface is developed using Python Tkinter module. The Tkinter module is an open-source module by which we can develop GUI screens hence, it is not software dependent nor vendor hardware. The OpenCV module used for image processing is interfaced using python.

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.

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.

scholarly journals Sistemas de reconocimiento facial, como herramienta para la búsqueda de personas

2017 ◽  
Vol 1 (8) ◽  
Author(s):  
Alex Gregorio Mendoza Arteaga ◽  
Gregorio Isoldo Mendoza Cedeño ◽  
Enrique Javier Macías Arias ◽  
Sandy Raúl Chun Molina
Keyword(s):  
Face Recognition ◽  
Key Words ◽  
Video Surveillance ◽  
Facial Recognition ◽  
Surveillance Systems ◽  
Biometric Recognition ◽  
Recognition Algorithms ◽  
Methods And Techniques ◽  
Automatic Systems ◽  
The Ideal

En el presente artículo se analiza la factibilidad de la implementación de algoritmos de reconocimiento facial integrados a los sistemas de video vigilancia de un territorio, para la localización de personas y como herramienta de búsqueda de individuos prófugos de la justicia convirtiéndose en un aporte importante a las investigaciones policiales y judiciales. Para alcanzar este objetivo, se estudian aristas sobre el reconocimiento biométrico y se considera el reconocimiento facial como el proceso ideal para la propuesta y discusión del artículo, en consecuencia, se investiga las etapas, métodos y técnicas más comunes y de mayor eficacia en los sistemas automáticos de reconocimiento de rostros para identificación de personas mediante imágenes y videos. Por consiguiente, se concluye que la implementación de un sistema automático de reconocimiento faciales interconectado a uno o varios sistemas de video vigilancia facilitara la búsqueda de individuos dentro del territorio donde se lo aplique.   Palabras claves: Biométrico, algoritmos, sistemas automáticos, tecnologías    Sistema de reconocimiento Facial    Systems of facial recognition, like tool for people's quest  Abstract In this article the feasibility of implementing facial recognition algorithms integrated video surveillance systems in a territory, to locate people tool analyzes and as individuals search for fugitives from justice becoming an important contribution to the police and judicial investigations. To achieve this goal, edges on biometric recognition are studied and considered facial recognition as the ideal for the proposal and discussion of Article process, therefore the steps, methods and techniques more common and more effective is investigated on the automatic face recognition to identify people through images and videos. Therefore, it is concluded that the implementation of a system of interconnected automatic facial recognition of one or several video surveillance systems facilitate finding individuals within the territory where it is applied.  Key words: Biometric, algorithms, automatic systems, technologies  

scholarly journals An Elementary Study on Various Techniques Involved in Face Recognition Systems : A Review

2019 ◽  
pp. 519-525
Author(s):  
Payal Maken
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Digital Image ◽  
Salient Feature ◽  
Recognition System ◽  
Human Face ◽  
Detection Techniques ◽  
Face Recognition System ◽  
The Face ◽  
Recognition Systems

Face recognition has now become one of the interesting fields of research and has received a substantial attention of researchers from all over the world. Face recognition techniques has been mostly used in the discipline of image analysis, image processing, etc. One of the face recognition techniques is used to develop a face recognition system to detect a human face in an image. In face recognition system a digital image with a human face is given as an input which extracts the significant features of face such as (eyes, nose, chin, cheeks, etc) to recognize a face in a digital image which is an exhausting task. Security of information is very salient feature and is difficult to achieve. Security cameras are present in offices, universities, banks, ATMs, etc. All these security cameras are embedded with face recognition systems. There are various algorithms which are used to solve this problem. This paper provides an overview of various techniques which are often used for this face recognition in a face recognition system. This paper is divided into five parts, first section concludes various face detection techniques, second section describes about image processing ,third section have details about face recognition techniques, fourth section describes various classification methods and last section concludes all of these sections.

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