New Deep Learning Model for Face Recognition and Registration in Distance Learning

Salamh, Ahmed B Salem; Akyüz, Halil İbrahim

doi:10.3991/ijet.v17i12.30377

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…Finally, as the unconstrained data sets demand better models, the approach presented shall be extended to the deep learning domain. Several studies in deep learning report higher accuracy like [37] [38] [39].…”

Section: Resultsmentioning

confidence: 99%

Ear Recognition Using Rank Level Fusion of Classifiers Outputs

Resmi

Joseph²,

Raju

et al. 2023

Int. J. Onl. Eng.

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An individual's authentication plays a vital role in our daily life. In the last decade, biometric-based authentication has become more prevalent than traditional approaches like passwords and pins. Ear recognition has gained attention in the biometric community in recent years. Researchers defined several features for the identification of a person from ear image. The features play a vital role in the success of classification models. This paper considers an ensemble of features for designing a new classification model. The features are assessed in isolation as well as through feature-level fusion. Subsequently, a rank-level fusion for classification is introduced. The experiments are conducted on both constrained and unconstrained ear datasets. The results are promising and open up new possibilities in machine learning-based ear recognition

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Section: Resultsmentioning

confidence: 99%

Ear Recognition Using Rank Level Fusion of Classifiers Outputs

Resmi

Joseph²,

Raju

et al. 2023

Int. J. Onl. Eng.

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“…Four different sets have been made available by the University of Essex through its Computer Vision Science Research Projects (Faces94, Faces95, Faces96, Grimace). 35,36 The grimace database has 18 male and 20 female participants, respectively, with an image resolution of 180×200 pixels. The primary expression variants are in the database.…”

Section: Resultsmentioning

confidence: 99%

“…Dataset description : In this work, various datasets such as Grimace, MUCT, and ORL are used to validate the performance and detection results of the proposed approach. Four different sets have been made available by the University of Essex through its Computer Vision Science Research Projects (Faces94, Faces95, Faces96, Grimace) 35,36 . The grimace database has 18 male and 20 female participants, respectively, with an image resolution of 180×200 pixels.…”

Section: Resultsmentioning

confidence: 99%

An adaptive gravitational search optimization (AGSO)‐based convolutional neural network—long short‐term memory (CNN‐LSTM) approach for face recognition and classification

Chengathir Selvi,

Maruthupandi,

Bhuvaneswari

et al. 2023

Concurrency and Computation

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SummaryFace recognition and classification have gained increasing attraction in the recent decades due to their widespread adoption in real time application systems. Most of the conventional research efforts focused on developing face recognition frameworks using enhanced optimization‐based classification methods, they are hampered by issues such as computational complexity, increased overhead, limited capacity to handle large datasets, and lengthy processing time. The novel contribution of this paper is to develop a highly competent and precise face recognition methodology through an innovative mechanism. In this framework, the initial step involves face detection from input images using an analytical face parts detection methodology. Subsequently, the tutor face filtering (TFF) technique is applied to preprocess the face image, enhancing its quality and filtering out noise content. Following this preprocessing step, features are extracted from the processed image using the direction‐based pattern extraction (DBPE) model. To improve classifier accuracy, a novel adaptive gravitational search optimization (AGSO) technique is employed to select the optimal features during model training. Finally, an integrated deep learning model, referred to as convolutional neural network—long short‐term memory (LSTM), is utilized for accurate face image recognition based on the selected optimal features. To assess and compare the system's performance, various metrics are employed in the results analysis to demonstrate the superiority of the proposed approach.

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“…It should be noted that biometric authentication is potentially vulnerable if it is used independently of classical authentication methods based on protocols using passwords and keys. A sufficient level of information security can only be ensured by combining classical and biometric authentication methods [17].…”

Section: Discussionmentioning

confidence: 99%

Biometric Authentication of Students to Control the Learning Process in Online Education

Muratuly

Denissova

Krak

et al. 2022

SJAITU

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This article considers the relevant problem of biometric authentication of students in higher educational institutions. The authors present the results of using a turnstile system with a face recognition terminal, with the ability to provide unique biometric data in real time. The study was conducted among students of the D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk, Kazakhstan. The article presents the results of studies of one of the biometric methods of personality recognition. In this method, the process of proving and verifying the identity of the person can be carried out through the presentation by the user of his biometric image. The processing results are sorted and compared with typical images from the database. With its positive decision, the developed software issues the results of biometric authentication of a person who presented himself in front of a digital scanner. The applied value of the results of the work lies in the possibility of using them in the field of education, and various industries to make a decision on providing access to information resources. In the course of the study, a technology was developed to provide biometric authentication processes for university students. Domestic and foreign scientists who have made a significant contribution to the development of methods for processing facial images are noted. A review of biometric methods of recognition is carried out, and tools for electronic authentication and modern information security systems are described. Factors that significantly affect the probability of correct recognition of students’ faces are determined. The analysis of ways to increase the probability of correct recognition of students by the image of the face is carried out.

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New Deep Learning Model for Face Recognition and Registration in Distance Learning

Cited by 7 publications

References 19 publications

Ear Recognition Using Rank Level Fusion of Classifiers Outputs

Ear Recognition Using Rank Level Fusion of Classifiers Outputs

An adaptive gravitational search optimization (AGSO)‐based convolutional neural network—long short‐term memory (CNN‐LSTM) approach for face recognition and classification

Biometric Authentication of Students to Control the Learning Process in Online Education

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