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Jana Alghamdi

Bio: Jana Alghamdi is an academic researcher from University of Dammam. The author has contributed to research in topics: Computer technology & Facial recognition system. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Papers
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Proceedings ArticleDOI
19 Mar 2020
TL;DR: 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.

8 citations


Cited by
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Proceedings ArticleDOI
05 Jun 2021
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.
Abstract: Earprint can be considered as one of the most important biometrics specially for phone communications. The significances of this paper is represented by a new established 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. The main challenge in this study is considered by establishing the EINTU database by collecting a big number of earprint images, where an acquisition device is designed and utilized. The DEL main results of 97.33% and 97.87% are attained for recognizing left and right earprints, respectively. The promising performances of this paper can motivate a future development of controlling communication mobile phone calls based on the recognized earprints.

3 citations

Proceedings ArticleDOI
22 Apr 2021
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.
Abstract: The paper is devoted to estimation of the effectiveness of the use of modern convolutional neural networks for face detection and face recognition. On standard and custom datasets, learning of neural networks and comparison of the effectiveness of their functioning are carried out. An algorithm and recommendations are proposed regarding the practical application of the neural networks for detecting faces on digital photographs.

3 citations

Book ChapterDOI
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.
Abstract: AbstractAttendance is a compulsory requirement of every organization. Maintaining attendance register manually on a regular basis is a tough and laborious task. Some of the available solution to the same are biometric, RFID, eye detection, voice recognition, and many more. This paper delivers an effective and smart process for detecting the presence and marking attendance. This 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.KeywordsArduinoPIR sensorMotion detectionESP32 camera

1 citations

25 Oct 2011
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.