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 A deep learning approach to building an intelligent video surveillance system

2020 ◽  
Author(s):  
Jie Xu
Keyword(s):  
Face Recognition ◽  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Single Shot ◽  
Convolutional Networks ◽  
Starting Point ◽  
Intelligent Video Surveillance System

Abstract Recent advances in the field of object detection and face recognition have made it possible to develop practical video surveillance systems with embedded object detection and face recognition functionalities that are accurate and fast enough for commercial uses. In this paper, we compare some of the latest approaches to object detection and face recognition and provide reasons why they may or may not be amongst the best to be used in video surveillance applications in terms of both accuracy and speed. It is discovered that Faster R-CNN with Inception ResNet V2 is able to achieve some of the best accuracies while maintaining real-time rates. Single Shot Detector (SSD) with MobileNet, on the other hand, is incredibly fast and still accurate enough for most applications. As for face recognition, FaceNet with Multi-task Cascaded Convolutional Networks (MTCNN) achieves higher accuracy than advances such as DeepFace and DeepID2+ while being faster. An end-to-end video surveillance system is also proposed which could be used as a starting point for more complex systems. Various experiments have also been attempted on trained models with observations explained in detail. We finish by discussing video object detection and video salient object detection approaches which could potentially be used as future improvements to the proposed system.

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.

REVIEW OF FACE RECOGNITION ALGORITHMS

2021 ◽  
Vol 140 (4) ◽  
pp. 209-243
Author(s):  
MICHAŁ BUKOWSKI
Keyword(s):  
Face Recognition ◽  
Hybrid Methods ◽  
Facial Recognition ◽  
Technical Problem ◽  
Three Dimensional ◽  
Facial Features ◽  
Automatic Assessment ◽  
Recognition Algorithms ◽  
Effi Ciency ◽  
Practical Effect

Information technology of the 20th and 21st centuries “opened the way” to the automatic assessment of anthropometric facial features, facial gestures and other characteristic behaviours. Recognition is a very complex technical problem with a signifi cant practical effect. There are dedicated applications for this purpose. The article presents face recognition algorithms for 2D images, for three-dimensional spaces, and methods using neural networks. Linear and nonlinear, local and global, and hybrid methods of facial recognition are presented. The study understands the strengths and weaknesses of the laws governing the use of face recognition technology and, if possible, analyses their effi ciency. The methodological review has been created in connection with the idea of the author’s own fast algorithms and facial recognition.

Application of robust face recognition in video surveillance systems

2018 ◽  
Vol 14 (2) ◽  
pp. 152-155 ◽  
Author(s):  
De-xin Zhang ◽  
Peng An ◽  
Hao-xiang Zhang
Keyword(s):  
Face Recognition ◽  
Video Surveillance ◽  
Surveillance Systems ◽  
Robust Face Recognition

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.

scholarly journals Analysis of Face Recognition Algorithm: Dlib and OpenCV

2020 ◽  
Vol 4 (1) ◽  
pp. 173-184
Author(s):  
Suwarno Suwarno ◽  
Kevin Kevin
Keyword(s):  
Face Recognition ◽  
Facial Recognition ◽  
Real Life ◽  
Recognition Algorithm ◽  
Speed Test ◽  
Recognition Algorithms ◽  
Image Dataset ◽  
In The Wild ◽  
Speed Accuracy ◽  
Speed And Accuracy

In face recognition there are two commonly used open-source libraries namely Dlib and OpenCV. Analysis of facial recognition algorithms is needed as reference for software developers who want to implement facial recognition features into an application program. From Dlib algorithm to be analyzed is CNN and HoG, from OpenCV algorithm is DNN and HAAR Cascades. These four algorithms are analyzed in terms of speed and accuracy. The same image dataset will be used to test, along with some actual images to get a more general analysis of how algorithm will appear in real life scenarios. The programming language used for face recognition algorithms is Python. The image dataset will come from LFW (Labeled Faces in the Wild), and AT&T, both of which are available and ready to be downloaded from the internet. Pictures of people around the UIB (Batam International University) is used for actual images dataset. HoG algorithm is fastest in speed test (0.011 seconds / image), but the accuracy rate is lower (FRR = 27.27%, FAR = 0%). DNN algorithm is the highest in level of accuracy (FRR = 11.69%, FAR = 2.6%) but the lowest speed (0.119 seconds / picture). There is no best algorithm, each algorithm has advantages and disadvantages.Keywords: Python, Face Recognition, Analysis, Speed, Accuracy.

scholarly journals Approaches to the Protection of Video Surveillance Systems When Applying Recognition Algorithms

2020 ◽  
Vol 14 (1) ◽  
pp. 18-22
Author(s):  
Tatiana Popova ◽  
◽  
Anatoly Afanasiev ◽  
Grigorij Zharkov ◽  
◽  
...  
Keyword(s):  
Video Surveillance ◽  
Surveillance Systems ◽  
Recognition Algorithms

scholarly journals Exploring Super-Resolution for Face Recognition

2021 ◽  
Vol 3 (1) ◽  
pp. 1-8
Author(s):  
Patrick Anderson Matias de Araújo ◽  
Eduardo Ferreira Ribeiro
Keyword(s):  
Facial Recognition ◽  
Super Resolution ◽  
Modern Society ◽  
Image Resolution ◽  
Biometric Recognition ◽  
Image Capture ◽  
Image Super Resolution ◽  
Resolution Algorithm ◽  
Bicubic Interpolation ◽  
The Ideal

Biometric recognition is part of many aspects of modern society. With the popularization of smartphones, facial recognition gains space in this environment of biometric technologies. With the diversity of image capture devices, of different brands and qualities, the images will not always be in the ideal standard to be recognized. This article tests and compares different scenarios and situations to assess the results obtained by facial recognition in different environments. For this, the quantity method of data analysis was used. In the first scenario, all images were submitted without changes. In the following, we have the reduction of image resolution, which may or may not be followed by enlargement to the original resolution via bicubic interpolation or through the Image Super-Resolution algorithm, these images can be all, or only that undergo tests. Results indicate that the first scenario obtained the best performance, followed by only the tests images change. The worst performance occurs where the properties of all images are affected. In situations where there is a reduction and enlargement are optional, the enlargement option performs better, so the bicubic enlargement has an advantage over the ISR, the situation in which only the reduction occurs has the worst performance.

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