ANALYSIS OF METHODS FOR DETECTING FACES IN AN IMAGE

2021 ◽  
pp. 77-88
Author(s):  
Zh. Sultanov
Keyword(s):  
Computer Vision ◽  
Template Matching ◽  
Vision System ◽  
Empirical Method ◽  
Modern Technology ◽  
Detection Methods ◽  
Human Face ◽  
Knowledge Based ◽  
Training Stage ◽  
The Face

In this article, computer vision is considered as modern technology of automatic processing of graphic images, and the relationship between the terms “computer vision” and “machine vision” is investigated. A diagram of a typical computer vision system is given and the possibility of using a system based on an artificial neural network for image analysis is considered. The article analyses the current situation with the use of computer vision systems and the possibility of its application. This article presents face recognition algorithms for existing categories, including: empirical method; feature method – invariant feature; use the template specified by the developer for identification; study the method of detecting the system by external signs. The empirical method of “top-down knowledge-based methods” involves creating an algorithm that implements a set of rules that image segments must satisfy in order to be recognized as faces. Feature-invariant approaches (Feature-invariant approaches) based on bottom-up knowledge constitute the second group of face detection methods. The methods of this group have the ability to recognize faces in different places as an advantage. Use the template set by the developer for identification (template matching method). Templates define specific standard images of face images, for example, describing the attributes of different areas of the face and their possible mutual positions. A method for detecting faces by external signs (a method for performing the training stage of the system by processing test images). The image (or its fragments) is somehow assigned a calculated feature vector, which is used to classify the image into two categories – human face/non-human face.

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

scholarly journals Human Face and Facial Parts Detection using Template Matching Technique

2020 ◽  
Vol 9 (4) ◽  
pp. 2296-2299
Keyword(s):  
Face Recognition ◽  
Template Matching ◽  
Cross Correlation ◽  
Human Face ◽  
Target Image ◽  
Matching Technique ◽  
Potential Applications ◽  
The Face ◽  
Execution Speed ◽  
Angle Interval

Face recognition has become relevant in recent years because of its potential applications. The aim of this paper is to find out the relevant techniques which give not only better accuracy also the efficient speed. There are several techniques available for face detection which give much better accuracy but the execution speed is not efficient. In this paper, a normalized cross-correlation template matching technique is used to solve this problem. According to the proposed algorithm, first different facial parts are detected likes mouth, eyes, and nose. If any of the two facial parts are found successfully then the face can be detected. For matching the templates with the target image, the template rotates at a certain angle interval.

Computer Vision and Pattern Recognition in Forensic Science

2019 ◽  
pp. 66-74
Author(s):  
Dmitrii Bakhteev
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Vision System ◽  
Modern Technology ◽  
Training Dataset ◽  
Network Training ◽  
Application Systems ◽  
Current State ◽  
History Of ◽  
Artificial Neural

The article discusses computer vision as a modern technology of automatic processing of graphic images, analyzes the relations between the terms «computer vision» and «machine vision». History of development of this technology is described, it occurred because of improvements in both computer technology and software. The computerization of forensic activities boils down to three areas: speeding up, simplifying, and improving the efficiency of information processing. A schema of a typical computer vision system is given, the possibility of using systems based on artificial neural networks for image analysis is considered. The current state of computer vision application systems and the possibility of its application in order to solve the problems of criminal justice are analyzed. The main areas of application of computer vision in forensic activities are identification of a person on the basis of his appearance, both during operational identification of a person and portrait examinations, photo and video examinations; quantitative assessment of objects in the image (for example, in case of calculating mass events’ participants); at preliminary and expert research of documents and their requisites; in functioning of criminal registration systems. Criteria and technical conditions for sampling signatures for creation of a training dataset for a neural network are given, the basics of developing an artificial neural network recognizing signs of signatures’ forgery is analyzed, which include three steps: creating a training dataset, adjusting weights and training priorities, testing the quality of network training.

The Tape Measurement System Based on Computer Vision Technology

2015 ◽  
Vol 738-739 ◽  
pp. 816-819
Author(s):  
Hong Zhou Li ◽  
Jie Lu ◽  
Jun Wang ◽  
Tao Jia
Keyword(s):  
Computer Vision ◽  
Template Matching ◽  
Vision System ◽  
Median Filter ◽  
Dark Field ◽  
Processing Technologies ◽  
Measurement Results ◽  
System Use ◽  
Dark Field Illumination ◽  
Computer Vision Technology

The paper devises a computer vision system based on virtual instruments to measure tape. The dark field illumination is chosen as Lampe-house in this system. Use Image processing technologies of Median filter, Image binarization, Template matching, Edge extraction to extract a reticle of tape. And compare it with standard tape enacted in this system to measure reticle error of the tape. Analyze various factors influencing the detection precision of the system. Tests show that the measurement results of this system are accurate, reliable and practicable.

scholarly journals Sistem Computer Vision Pengenalan Pola Angka dan Operator Matematika Pada Permainan Kartu Angka Berbasis Jaringan Syaraf Tiruan Perceptron

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Rian Rahmanda Putra ◽  
Fery Antony
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Image Processing Techniques ◽  
Perceptron Algorithm ◽  
Card Number ◽  
Training Stage ◽  
Processing Techniques ◽  
Real Time Application

<p align="center"><strong><em>Abstract <br /></em></strong></p><p><em>Computer vision is an image processing by a computer to obtain information from image captured through the camera generally used in real-time application. This paper reports on the results of research conducted on computer vision system designed to be able to recognize the image number (0-9) and mathematical operators (addition (+) and subtraction (-)) in a card number figures. Computer vision system designed in this study consists of a camera on the android phone that used to captured images on the card number and the computer that has artificial neural network perceptron algorithm in identifiying images. Both components of the computer vision system are connected wirelessly through the TCP/IP Protocol. At the training stage of Perceptron ANN, 10 samples for each number and mathematical operators are used. Computer vision system built in this study also have several image processing techniques such as greyscalling, thresholding, cropping and resizing. This techniques is used to filter the information from the images captured by camera in order to get the adequate and smaller image to be processed by ANN Perceptron. Stages of testing performed three times. First testing is given picture numbers 0-3, second testing is given picture number 4-7 and third testing is given number 8-9, addition symbol and subtraction symbol. Based on testing result, system built are able to recognize 10 from 12 image rendered with a success rate of 83.33%.</em></p><p><strong><em>Keywords</em></strong><em> : Computer vision, perceptron, card number</em></p><p><em> </em></p><p align="center"><strong><em>Abstrak <br /></em></strong></p><p><em>Computer vision merupakan proses pengolahan citra oleh computer untuk mendapatkan informasi dari citra yang ditangkap melalui kamera yang umumnya digunakan pada aplikasi waktu nyata. Tulisan ini melaporkan tentang hasil penelitian yang dilakukan tentang sistem computer vision yang dirancang untuk dapat mengenali gambar angka (0-9) dan operator matematika(penjumlahan (+) dan pengurangan (-)) pada permainan kartu angka. Sistem computer vision yang dirancang pada penelitian ini terdiri dari kamera pada ponsel android yang digunakan untuk menangkap gambar pada kartu angka dan komputer yang memiliki algoritama Jaringan Syaraf Tiruan Perceptron dalam melakukan identifikasi gambar. Kedua komponen sistem computer vision tersebut dihubungkan memlaui jaringan wireless melalui protocol TCP/IP. Pada tahapan pelatihan JST perceptron, digunakan 10 sample citra untuk masing – masing angka dan operator matematika yang akan dikenali oleh sistem. Pada penelitian ini juga dilakukan tahapan pemrosesan citra sebelum diolah oleh JST Perceptron baik dalam tahapan pelatihan maupun pada saat sistem dijalankan. Tahapan pengolahan citra yang digunakan pada penelitian ini adalah greyscalling, thresholding, cropping dan resizing. Hal ini dilakukan untuk menyaring informasi pada citra yang ditangkap oleh kamera agar didapatkan citra yang berukuran kecil dengan  informasi yang lengkap untuk diproses oleh JST Perceptron. Pada saat sistem diuji coba, diberikan 4 deret kartu angka di depan kamera. Pada pengujian pertama diberikan gambar angka 0-3, pengujian kedua diberikan gambar angka 4-7 dan pada pengujian ketiga diberikan angka 8-9 serta gambar operator penjumlahan dan pengurangan. Berdasarkan pengujian yang dilakukan, sistem computer vision yang dirancang mampu mengenali 10dari 12 gambar yang diberikan dengan tingkat keberhasilan sebesar 83.33%.</em></p><p><strong><em>Kata Kunci </em></strong><em>: computer vision, perceptron, kartu angka</em></p>

scholarly journals Recognition of Human Face Emotions Detection Using Computer Vision Based Smart Images

2021 ◽  
Vol 10 (3) ◽  
pp. 1700-1704
Keyword(s):  
Computer Vision ◽  
Skin Color ◽  
High Impact ◽  
Human Face ◽  
Automated Identification ◽  
Desktop Application ◽  
Human Face Detection ◽  
The Face ◽  
Impact Area ◽  
Face Detector

Humans share a collection of basic and essential emotions that are expressed by facial expressions that seem to be consistent. The automated identification of humanemotion in imageswill be possible due to an algorithm that detects, extracts, and evaluates these facial expressions.The Face detector and recognizer application is a desktop application used to recognize human face emotions by using a computer vision based smart images. It consists of human face detection picture boxes and considers an image as an original image. It climaxes a face skin color, finds high impact area and identifies different emotions of the face from an image. The results of the image are depending upon the separation of Eyes & Lips movement of person. This is done by comparingface embedding vectors. It finds the smart photos focused on computer vision for successful identification of facial emotions in terms of various modes of speech such as smiling, shocking and weeping.

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