scholarly journals Identifikasi Wajah Dengan Segmentasi Warna Kulit Menggunakan Metode Viola Jones

2019 ◽  
Vol 4 (1) ◽  
pp. 1-6
Author(s):  
Andi Asni b ◽  
Tamara Octa Dana
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Skin Color ◽  
Detection System ◽  
Color Segmentation ◽  
Face Images ◽  
Detection Of Objects ◽  
The Face ◽  
Left And Right ◽  
Normal Light

Abstract - Face detection (face detection) is one of the initial steps that is very important before the face recognition process (face recognition). Face detection is the detection of objects in the form of faces in which there are special features that represent the shape of faces in general. One method of face detection is the Viola Jones method. Viola Jones method is used to detect faces and skin color segmentation, test data processing using Matlab and capture on a Smartphone. The test is carried out at normal light intensity with a predetermined distance and face position. The results of this study indicate the level of accuracy of detection of face image variations in the position of face images facing forward (frontal), sideways left and right 45̊. But it has a weakness of this face detection system that is unable to determine faces in images that have faces that are not upright (tilted) or not frontal (facing sideways) at a 90̊ angle. Face position that is upright / not upright will determine the success of this face detection. The level of identification of the Viola Jones simulation was 100% with 4 images consisting of 3 boys and 1 girl.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

The System of Face Recognition Design

2014 ◽  
Vol 644-650 ◽  
pp. 3943-3946
Author(s):  
Xiao Bin Yu ◽  
Zi Qiao Li ◽  
Wen Qiang Ke ◽  
Rui Peng Li ◽  
Kai Xiong
Keyword(s):  
Face Recognition ◽  
Skin Color ◽  
Color Space ◽  
Color Model ◽  
Face Images ◽  
Accurate Detection ◽  
Haar Features ◽  
The Face ◽  
The Media ◽  
Skin Color Model

The technology of face recognition is the media to face images as the identity of the face recognition system.Through the choice of color space and the establishment of skin color model, give a rough detection for the human's image, then use the face Haar features getting more accurate detection.

A Face Detection Algorithm in Color Image Based on Skin Color Segmentation

2013 ◽  
Vol 811 ◽  
pp. 417-421
Author(s):  
Shi Lei
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Image ◽  
Detection Algorithm ◽  
Color Segmentation ◽  
Skin Region ◽  
Otsu Method ◽  
The Face ◽  
Segmentation Image ◽  
Ellipse Area

Aiming at color images under complex background, this paper put forward a face detection algorithm based on skin color segmentation, combining the geometric characteristics. The skin region can be obtained by using skin color model and OTSU method to automatically optimize threshold segmentation image. By analyzing the characteristics of skin color region, the face position is determined by criterion of ellipse area.

scholarly journals Security Implementation by Machine Learning Based Face Recognization with SVM

2021 ◽  
pp. 214-218
Author(s):  
Apurva Yawalikar ◽  
U. W. Hore
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Detection System ◽  
Object Class ◽  
Detection Systems ◽  
The Face ◽  
Object Class Detection ◽  
Human Faces ◽  
Work Done

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given. As per the various face detection system seen various work done onto the detection with various way. In existing this are get evaluate with the HOG with SVM, which will help us to get the exact value so that it is necessary to implement the system which will more effective and advance. As per the face detection seen there are various face detection systems are implemented. Determining face is easy but recognition is quite typical so that we are proposed machine learning based face recognition with SVM which helps to determine and detect the faces So the proposed system will get integrated with highly efficient and effective SVM model for face recognition. The proposed methodology will help us to implement the face based security implementation in any security system like door lock, mobile screen lock etc.

scholarly journals ANALYSIS OF HUMAN FACE RECOGNITION ALGORITHM USING PCA+FDIT IN IMAGE DATABASE FOR CRIME INVESTIGATION

2013 ◽  
Vol 4 (3) ◽  
pp. 788-796
Author(s):  
V. S. Manjula
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Face Detection ◽  
Skin Color ◽  
Back Propagation ◽  
Real Life ◽  
Hierarchical Architecture ◽  
Detection And Identification ◽  
The Face ◽  
Real Time Application

In general, the field of face recognition has lots of research that have put interest in order to detect the face and to identify it and also to track it. Many researchers have concentrated on the face identification and detection problem by using various approaches. The proposed approach is further very useful and helpful in real time application. Thus the Face Detection, Identification  which is proposed here is used to detect the faces in videos in the real time application by using the FDIT (Face Detection Identification Technique) algorithm. Thus the proposed mechanism is very help full in identifying individual persons who are been involved in the action of robbery, murder cases and terror activities. Although in face recognition the algorithm used is of histogram equalization combined with Back propagation neural network in which we recognize an unknown test image by comparing it with the known training set images that are been stored in the database. Also the proposed approach uses skin color extraction as a parameter for face detection. A multi linear training and rectangular face feature extraction are done for training, identifying and detecting.   Thus the proposed technique   is PCA + FDIT technique configuration only improved recognition for subjects in images are included in the training data.   It is very useful in identify a single person from a group of faces.   Thus the proposed technique is well suited for all kinds faces frame work for face detection and identification. The face detection and identification modules share the same hierarchical architecture. They both consist of two layers of classifiers, a layer with a set of component classifiers and a layer with a single combination classifier.  Also we have taken a real life example and simulated the algorithms in IDL Tool successfully.

scholarly journals Face Detection in a Mixed-Subject Document

2016 ◽  
Vol 6 (6) ◽  
pp. 2828
Author(s):  
Lhoussaine Bouhou ◽  
Rachid El Ayachi ◽  
Mohamed Baslam ◽  
Mohamed Oukessou
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Template Matching ◽  
Detection System ◽  
Second Phase ◽  
Text And Image ◽  
Important Research Topic ◽  
The Face ◽  
Second Category ◽  
Text Images

<p>Before you recognize anyone, it is essential to identify various characteristics variations from one person to another. among of this characteristics, we have those relating to the face. Nowadays the detection of skin regions in an image has become an important research topic for the location of a face in the image. In this research study, unlike previous research studies  related  to  this  topic  which  have  focused  on  images  inputs  data  faces,  we  are  more interested to the fields face detection in mixed-subject documents (text + images). The face detection system developed is based on the hybrid method to distinguish two categories of objects from the mixed document. The first category is all that is text or images containing figures having no skin color, and the second category is any figure with the same color as the skin. In the second phase the detection system is based on Template Matching method to distinguish among the figures of the second category only those that contain faces to detect them. To validate this study, the system developed is tested on the various documents which including text and image.</p>

scholarly journals A smart login system using face detection and recognition by ORB algorithm

2020 ◽  
Vol 20 (2) ◽  
pp. 1078
Author(s):  
Mohammad Jahangir Alam ◽  
Tanjia Chowdhury ◽  
Md. Shahzahan Ali
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Punch Card ◽  
Effective Technique ◽  
Face Images ◽  
Different Types ◽  
Web Camera ◽  
The Face ◽  
Face Detection And Recognition ◽  
Human Faces

<p>We can identify human faces using a web Camera which is known as Face Detection.  This is a very effective technique in computer technology. There are used different types of attendance systems such as log in with the password, punch card, fingerprint, etc. In this research, we have introduced a facial recognition type of biometric system that can identify a specific face by analyzing and comparing patterns of a digital image.  This system is the latest login system based on face detection. Primarily, the device captures the face images and stores the captured images into the specific path of the computer relating the information into a database. When any body tries to enter into any room or premises through this login system, the system captures the image of that particular person and matches the image with the stored image. If this image matches with the stored image then the system allows the person to enter the room or premises, otherwise the system denies entry. This face recognition login system is very effective, reliable and secured. This research has used the Viola and Jones algorithm for face detection and ORB for image matching in face recognition and Java, MySql, OpenCV, and iReport are used for implementation.</p>

Face Recognition Based on Symmetrical Half-Join Method using Stereo Vision Camera

2016 ◽  
Vol 6 (6) ◽  
pp. 2818 ◽  
Author(s):  
Edy Winarno ◽  
Agus Harjoko ◽  
Aniati Murni Arymurthy ◽  
Edi Winarko
Keyword(s):  
Face Recognition ◽  
Stereo Vision ◽  
Recognition Rate ◽  
Recognition System ◽  
Face Images ◽  
Face Recognition System ◽  
Proposed Model ◽  
The Face ◽  
Left And Right ◽  
Recognition Technique

<p>The main problem in face recognition system based on half-face pattern is how to anticipate poses and illuminance variations to improve recognition rate. To solve this problem, we can use two lenses on stereo vision camera in face recognition system. Stereo vision camera has left and right lenses that can be used to produce a 2D image of each lens. Stereo vision camera in face recognition has capability to produce two of 2D face images with a different angle. Both angle of the face image will produce a detailed image of the face and better lighting levels on each of the left and right lenses. In this study, we proposed a face recognition technique, using 2 lens on a stereo vision camera namely symmetrical half-join. Symmetrical half-join is a method of normalizing the image of the face detection on each of the left and right lenses in stereo vision camera, then cropping and merging at each image. Tests on face recognition rate based on the variety of poses and variations in illumination shows that the symmetrical half-join method is able to provide a high accuracy of face recognition and can anticipate variations in given pose and illumination variations. The proposed model is able to produce 86% -97% recognition rate on a variety of poses and variations in angles between 0 °- 22.5 °. The variation of illuminance measured using a lux meter can result in 90% -100% recognition rate for the category of at least dim lighting levels (above 10 lux).</p>

Face Detection in a Mixed-Subject Document

2016 ◽  
Vol 6 (6) ◽  
pp. 2828
Author(s):  
Lhoussaine Bouhou ◽  
Rachid El Ayachi ◽  
Mohamed Baslam ◽  
Mohamed Oukessou
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Template Matching ◽  
Detection System ◽  
Second Phase ◽  
Text And Image ◽  
Important Research Topic ◽  
The Face ◽  
Second Category ◽  
Text Images

<p>Before you recognize anyone, it is essential to identify various characteristics variations from one person to another. among of this characteristics, we have those relating to the face. Nowadays the detection of skin regions in an image has become an important research topic for the location of a face in the image. In this research study, unlike previous research studies  related  to  this  topic  which  have  focused  on  images  inputs  data  faces,  we  are  more interested to the fields face detection in mixed-subject documents (text + images). The face detection system developed is based on the hybrid method to distinguish two categories of objects from the mixed document. The first category is all that is text or images containing figures having no skin color, and the second category is any figure with the same color as the skin. In the second phase the detection system is based on Template Matching method to distinguish among the figures of the second category only those that contain faces to detect them. To validate this study, the system developed is tested on the various documents which including text and image.</p>

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