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Proceedings ArticleDOI

A Survey On Face Recognition Algorithms

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TLDR
The main objective of this survey paper is to compare the multiple algorithms used for facial recognition.
Abstract
Facial Recognition System is a computer technology that uses a variety of algorithms that identify the human face in digital images, identify the person and then verify the captured images by comparing them with the facial images stored in the database. Facial recognition is an important topic in computer vision, and many researchers have studied this topic in many different ways; it is important especially in some applications such as surveillance systems. The main objective of this survey paper is to compare the multiple algorithms used for facial recognition.

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Citations
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Proceedings ArticleDOI

Individual Recognition with Deep Earprint Learning

TL;DR: In this article, a new earprint database named the Earprint Images for Northern Technical University (EINTU) and a suggested deep learning (DL) model for personal verification called the Deep Earprint Learning (DEL) network are presented.
Proceedings ArticleDOI

The Neural Network Models Effectiveness for Face Detection and Face Recognition

TL;DR: In this article, the effectiveness of the use of modern convolutional neural networks for face detection and face recognition is evaluated on both standard and custom datasets, learning of neural networks and comparison of their effectiveness are carried out.
Book ChapterDOI

Arduino and ESP32-CAM-Based Automatic Touchless Attendance System

TL;DR: In this article , a system uses motion detection and capture images and send those captured images to the respective mail id with the confirmation that the attendance has been received, the detailed explanation of the image capturing while motion is detected, and saving the images in a micro SD card is elaborated along with email sending procedure.

Face Recognition with PCA and KPCA using Elman Neural Network and SVM

Abstract: In this paper, in order to categorize ORL database face pictures, principle Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) methods by using Elman neural network and Support Vector Machine (SVM) categorization methods are used. Elman network as a recurrent neural network is proposed for modeling storage systems and also it is used for reviewing the effect of using PCA numbers on system categorization precision rate and database pictures categorization time. Categorization stages are conducted with various components numbers and the obtained results of both Elman neural network categorization and support vector machine are compared. In optimum manner 97.41% recognition accuracy is obtained. Keywords—Face recognition, Principal Component Analysis, Kernel Principal Component Analysis, Neural network, Support Vector Machine.
References
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Proceedings ArticleDOI

Robust real-time face detection

TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.
Proceedings ArticleDOI

WIDER FACE: A Face Detection Benchmark

TL;DR: There is a gap between current face detection performance and the real world requirements, and the WIDER FACE dataset, which is 10 times larger than existing datasets is introduced, which contains rich annotations, including occlusions, poses, event categories, and face bounding boxes.
Journal ArticleDOI

Improving face recognition by elman neural network using curvelet transform and HSI color space

TL;DR: The proposed algorithm was studied on the images of 20 students from the Department of Computer Science, Tikrit University and the rate of face recognition was 94%.
Journal Article

Face Recognition with PCA and KPCA using Elman Neural Network and SVM

TL;DR: Categorization stages are conducted with various components numbers and the obtained results of both Elman neural network categorization and support vector machine are compared and in optimum manner 97.41% recognition accuracy is obtained.
Journal ArticleDOI

Smart Attendance Marking System using Facial Recognition

TL;DR: This paper proposed an application of facial recognition in attendance marking system which is primarily based face detection and recognition algorithms mechanically detect the student and mark the attendance when he enters the school room.
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