Automated Attendance System Through Eigen Faces Using Image Processing

2016 ◽  
Vol 5 (3) ◽  
pp. 111
Author(s):  
Meduri Sree Vidya
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Low Cost ◽  
Human Face ◽  
Face Space ◽  
Nodal Points ◽  
The Face ◽  
Low Dimensional

Eigen faces(PCA) approach for face recognition ,The face is an important part of who you are how people identify you. In face recognition there are two types of comparisons verification and identification. There are about 80 nodal points on a human face here are few nodal points that are measured by software that are distance between eyes ,width of the nose, Depth of the eye socket, Check bones, Jaw line and Chin By this method we can take automatic attendance .face recognition is done by projecting new image onto a low dimensional linear “face space” defined by the Eigen faces. This method is reliable , low cost , faster access and reduce man power.

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.

scholarly journals Opening Doors Using Internet Of Things (IoT) Based Face Recognition

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Wahyu Ariansyah ◽  
Dirja Nur Ilham ◽  
Khairuman Khairuman ◽  
Rudi Arif Candra
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Face Recognition ◽  
Internet Of Things ◽  
Digital Image Processing ◽  
Recognition System ◽  
Human Face ◽  
Face Recognition System ◽  
The Face ◽  
Primary Means

Face recognition is a digital image processing approach that uses face photographs as input to identify a person. Face recognition is important since the face is a person's primary means of identification because the shape of a person's face differs significantly, which is easy to do intuitively using the visual senses. Image processing, face detection, feature extraction, and classification are all aspects of the face recognition system, which seeks to determine whether the image obtained is a person's face stored in the database. Principles of operation If a human face appears in front of the camera, the system quickly executes a facial recognition procedure and compares the face to facial data kept on the website. If a face detected by the camera matches the face stored on the website, the solenoid will automatically be in the on position or the door will be open, and vice versa, if the face detected by the camera does not match, the solenoid will remain in the off position or the door will remain locked. This tool can be used to improve the security system on the door of a private room or a room that can only be accessed by certain people.

Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

2017 ◽  
Vol 7 (7) ◽  
pp. 302
Author(s):  
Reshma P ◽  
Muneer VK ◽  
Muhammed Ilyas P
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
High Accuracy ◽  
Efficient Technique ◽  
Human Face ◽  
Traditional Methods ◽  
Person Recognition ◽  
The Face

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.

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.

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.

The Face-Space Typicality Paradox: Understanding the Face-Space Metaphor

1998 ◽  
Vol 51 (3) ◽  
pp. 475-483 ◽  
Author(s):  
A. Mike Burton ◽  
John R. Vokey
Keyword(s):  
Face Processing ◽  
Dimensional Space ◽  
Short Note ◽  
High Dimensional ◽  
Human Face ◽  
Face Space ◽  
The Face

Some recent accounts of human face processing use the idea of “face space”, considered to be a multi-dimensional space whose dimensions correspond to ways in which faces can vary. Within this space, “typicality” is sometimes taken to reflect the proximity of a face to its local neighbours. Intuitions about the distribution of faces within the space may suggest that the majority of faces will be “typical” in these terms. However, when typicality measures are taken, researchers very rarely find that faces cluster at the “typical” end of the scale. In this short note we attempt to resolve this paradox and point out that reasoning about high dimensional distributions requires that some specific assumptions are made explicit.

Low-dimensional representation of faces in higher dimensions of the face space

1993 ◽  
Vol 10 (3) ◽  
pp. 405 ◽  
Author(s):  
A. J. O’Toole ◽  
K. A. Deffenbacher ◽  
D. Valentin ◽  
H. Abdi
Keyword(s):  
Higher Dimensions ◽  
Dimensional Representation ◽  
Face Space ◽  
The Face ◽  
Low Dimensional

Human Face Recognition and the Face Image Set′s Topology

1994 ◽  
Vol 59 (2) ◽  
pp. 254-261 ◽  
Author(s):  
M. Bichsel ◽  
A.P. Pentland
Keyword(s):  
Face Recognition ◽  
Face Image ◽  
Human Face ◽  
Human Face Recognition ◽  
The Face ◽  
Image Set

scholarly journals Instagram Image Filtration with Computer Vision

2021 ◽  
Author(s):  
Tanjimul Ahad Asif ◽  
Baidya Nath Saha
Keyword(s):  
Image Processing ◽  
Human Face ◽  
Image Processing Techniques ◽  
Search Results ◽  
Search Result ◽  
Face Images ◽  
Image Filtration ◽  
The Face ◽  
Processing Techniques ◽  
Human Faces

Instagram is one of the famous and fast-growing media sharing platforms. Instagram allows users to share photos and videos with followers. There are plenty of ways to search for images on Instagram, but one of the most familiar ways is ’hashtag.’ Hashtag search enables the users to find the precise search result on Instagram. However, there are no rules for using the hashtag; that is why it often does not match the uploaded image, and for this reason, Users are unable to find the relevant search results. This research aims to filter any human face images on search results based on hashtags on Instagram. Our study extends the author’s [2] work by implementing image processing techniques that detect human faces and separate the identified images on search results based on hashtags using the face detection technique.

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