scholarly journals A Review on Online Classroom Attendance Marking System Using Face Recognition, Python, Computer Vision, and Digital Image Processing

2021 ◽  
Vol 9 (12) ◽  
pp. 2342-2345
Author(s):  
Nandkishor Satpute
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Face Recognition ◽  
Processing Method ◽  
Online Classroom ◽  
Detection Algorithms ◽  
The Neural Network ◽  
Network Concept ◽  
The Face ◽  
Human Faces

Abstract: The face is that the identity of someone. The tactic to appear out this physical feature has seen an exquisite change since the advent of the image processing method. Attendance is monitored in every school, college and library. The regular method for attendance is for teachers to call student name & mark attendance. Nowadays, AI has been explored for computer vision-related applications. So, we use the neural network concept in Face recognition for automatically attendance marking systems. This project will perform the face recognition and face detection algorithms, to generate the computer systems strength of acquiring and recognizing human faces fast, accurately, and precisely in live streams so that the systems can be used in the marking attendance

scholarly journals Perspective Correction For Image Processing

2015 ◽  
Vol 21 (3) ◽  
pp. 712-721
Author(s):  
Romana Oancea ◽  
Vlad Bârsan ◽  
Cristian Molder
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
The Face ◽  
Human Faces

Abstract The paper proposes an algorithm for applying the perspective correction of images containing human faces. Based on the coordinates of the two eyes, of the center of the mouth and of a point on the line of the mouth, one can estimate the angle of inclination of the face. After applying the perspective correction, the area corresponding to the face can be used by a face recognition algorithm based on aspect in order to improve the good recognition rate.

Algorithmic Analysis of Automatic Attendance System using Facial Recognition

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Face Recognition ◽  
Daily Life ◽  
Real Life ◽  
Student Attendance ◽  
Surveillance Camera ◽  
Daily Life Activities ◽  
The Face ◽  
Algorithmic Analysis ◽  
Human Faces ◽  
Face Biometrics

Attendance management can become a tedious task for teachers if it is performed manually.. This problem can be solved with the help of an automatic attendance management system. But validation is one of the main issues in the system. Generally, biometrics are used in the smart automatic attendance system. Managing attendance with the help of face recognition is one of the biometric methods with better efficiency as compared to others. Smart Attendance with the help of instant face recognition is a real-life solution that helps in handling daily life activities and maintaining a student attendance system. Face recognition-based attendance system uses face biometrics which is based on high resolution monitor video and other technologies to recognize the face of the student. In project, the system will be able to find and recognize human faces fast and accurately with the help of images or videos that will be captured through a surveillance camera. It will convert the frames of the video into images so that our system can easily search that image in the attendance database.

Face Recognition with Active Appearance Model (AAM)

2013 ◽  
pp. 1124-1144 ◽  
Author(s):  
Patrycia Barros de Lima Klavdianos ◽  
Lourdes Mattos Brasil ◽  
Jairo Simão Santana Melo
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Face Recognition ◽  
Face Processing ◽  
Research Field ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Human Face ◽  
Active Appearance ◽  
Human Faces

Recognition of human faces has been a fascinating subject in research field for many years. It is considered a multidisciplinary field because it includes understanding different domains such as psychology, neuroscience, computer vision, artificial intelligence, mathematics, and many others. Human face perception is intriguing and draws our attention because we accomplish the task so well that we hope to one day witness a machine performing the same task in a similar or better way. This chapter aims to provide a systematic and practical approach regarding to one of the most current techniques applied on face recognition, known as AAM (Active Appearance Model). AAM method is addressed considering 2D face processing only. This chapter doesn’t cover the entire theme, but offers to the reader the necessary tools to construct a consistent and productive pathway toward this involving subject.

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.

Adaptive GLOH with PSO-trained NN for the recognition of plastic surgery faces and their types

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Archana Harsing Sable ◽  
Sanjay N. Talbar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Plastic Surgery ◽  
Local Binary Pattern ◽  
Principal Component ◽  
Traditional Learning ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Orientation Histogram ◽  
The Face

Abstract Numerous algorithms have met complexity in recognizing the face, which is invariant to plastic surgery, owing to the texture variations in the skin. Though plastic surgery serves to be a challenging issue in the domain of face recognition, the concerned theme has to be restudied for its hypothetical and experimental perspectives. In this paper, Adaptive Gradient Location and Orientation Histogram (AGLOH)-based feature extraction is proposed to accomplish effective plastic surgery face recognition. The proposed features are extracted from the granular space of the faces. Additionally, the variants of the local binary pattern are also extracted to accompany the AGLOH features. Subsequently, the feature dimensionality is reduced using principal component analysis (PCA) to train the artificial neural network. The paper trains the neural network using particle swarm optimization, despite utilizing the traditional learning algorithms. The experimentation involved 452 plastic surgery faces from blepharoplasty, brow lift, liposhaving, malar augmentation, mentoplasty, otoplasty, rhinoplasty, rhytidectomy and skin peeling. Finally, the proposed AGLOH proves its performance dominance.

scholarly journals SP680TO CREATE A FACE MAP FOR SAGLIKER SYNDROME (SS) BY USING IMAGE PROCESSING METHOD AND HOURGLASS APPEARANCE SIGN ON THE FACE

2015 ◽  
Vol 30 (suppl_3) ◽  
pp. iii602-iii603
Author(s):  
Mahmut Dingil ◽  
Yahya Sagliker ◽  
Hasan Sabit Sagliker ◽  
Nuray Paylar ◽  
Selim Kapur
Keyword(s):  
Image Processing ◽  
Processing Method ◽  
Image Processing Method ◽  
The Face

scholarly journals ILLUMINATION PROCESSING IN FACE RECOGNITION

2014 ◽  
Vol 28 (05) ◽  
pp. 1456011 ◽  
Author(s):  
ZHENXUE CHEN ◽  
CHENGYUN LIU ◽  
FALIANG CHANG ◽  
XUZHEN HAN ◽  
KAIFANG WANG
Keyword(s):  
Adverse Effect ◽  
Face Recognition ◽  
Light Intensity ◽  
Negative Impact ◽  
Processing Method ◽  
Dark Region ◽  
Light Region ◽  
The Face ◽  
Recognition Systems ◽  
Better Than

Changes in light intensity and angle present a major challenge to the creation of reliable face recognition systems. The existence of bright regions and dark regions has been shown to have a serious negative impact on the performance of face recognition systems. This paper proposes a solution to this problem based on self-quotient image (SQI) processing method. In this method, bright and dark areas are processed separately without changing the essential characteristics of the image of the face. The dark and light areas are processed separately by SQI. Experimental results indicate that this Single-Light-Region and Single-Dark-Region SQI method removes the adverse effect of multi-bright and multi-dark areas better than competing methods.

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.

scholarly journals Face recognition attendance system using Local Binary Pattern (LBP)

2019 ◽  
Vol 8 (1) ◽  
pp. 239-245 ◽  
Author(s):  
Shamsul J. Elias ◽  
Shahirah Mohamed Hatim ◽  
Nur Anisah Hassan ◽  
Lily Marlia Abd Latif ◽  
R. Badlishah Ahmad ◽  
...  
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Automated System ◽  
Student Attendance ◽  
Recognition Method ◽  
Face Recognition System ◽  
The Face

Attendance is important for university students. However, generic way of taking attendance in universities may include various problems. Hence, a face recognition system for attendance taking is one way to combat the problem. This paper will present an automated system that will automatically saves student’s attendance into the database using face recognition method. The paper will elaborate on student attendance system, image processing, face detection and face recognition. The face detection part will be done by using viola-jones algorithm method while the face recognition part will be carried on by using local binary pattern (LBP) method. The system will ensure that the attendance taking process will be faster and more accurate.

scholarly journals Development of an algorithm for object access system automation based on face recognition using a neural network

Connectivity ◽  
2020 ◽  
Vol 145 (3) ◽  
Author(s):  
V. S. Orlenko ◽  
◽  
I. I. Kolosinsʹkyy
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Neural Model ◽  
Large Datasets ◽  
Second Step ◽  
Test Set ◽  
Access System ◽  
The Neural Network ◽  
The Face ◽  
Face Search

The article deals with the technical side of face recognition — the neural network. The advantages of the neural network for identification of the person are substantiated, the stages of comparison of two images are considered. The first step is defined as the face search in the photo. Using several tests, the best neural network was identified, which allowed to effectively obtain a normalized image of a person’s face. The second step is to find the features of the person, for which the comparative analysis is performed. It was this stage that became the main point in this article — 16 sets of tests were carried out, each test set has 12 tests inside. Two large datasets were used for the study to evaluate the effectiveness of the algorithms not only in ideal circumstances but also in the field. The results of the study allowed us to determine the best method and neural model for finding a face and dividing it into parts. It is determined which part of the face the algorithm recognizes best — it will allow making adjustments to the location of the camera.

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