End-to-End CNN conceptual model for a biometric authentication mechanism for ATM machines
Abstract People commonly use automated teller machines, also known as ATMs, in contemporary culture. The use of ATMs for currency withdrawal is becoming increasingly widespread in the modern world. Furthermore, attacks and break-ins have increased, highlighting the need for an ATM that is more secur...
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Discover Electronics |
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| Online Access: | https://doi.org/10.1007/s44291-024-00034-x |
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| _version_ | 1850129772672188416 |
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| author | Karthikeyan Velayuthapandian Natchiyar Murugan Saranya Paramasivan |
| author_facet | Karthikeyan Velayuthapandian Natchiyar Murugan Saranya Paramasivan |
| author_sort | Karthikeyan Velayuthapandian |
| collection | DOAJ |
| description | Abstract People commonly use automated teller machines, also known as ATMs, in contemporary culture. The use of ATMs for currency withdrawal is becoming increasingly widespread in the modern world. Furthermore, attacks and break-ins have increased, highlighting the need for an ATM that is more secure and equipped with extra safety measures. The existing method of confirming these transactions through credit cards and ATMs carries a high level of risk. Card trapping, cash larceny, shoulder surfing, and ATM skimming are among the examples. The proposed system, which is based on biometrics and deep convolutional neural networks, can effectively resolve the problems associated with conventional ATM cards and PINs. The proposed method relies on face biometrics, which offers a novel technique to address the current challenge. This article presents a novel end-to-end multiscale attention-based light-weighted deep convolutional neural network (MA-LW-DCNN) framework for enhancing the accuracy of recognizing faces. The multiscale attention mechanism in face recognition enhances feature extraction by applying convolution filters at various scales, allowing the network to capture detailed and global facial features. It generates an attention map by normalizing characteristic maps and refining them using activation functions, improving recognition accuracy by focusing on essential face regions. The suggested system is assessed using a synthetic dataset against conventional machine and deep learning-based face recognition techniques. The experiment results for different individuals demonstrate an accuracy rate of around 99.84% in authenticating test samples. The proposed approach produced significantly improved evaluation measures precision, recall, and loss by 0.99, 0.99, and 0.16, respectively. |
| format | Article |
| id | doaj-art-7c6e4e315b4b49d08e6252655cfc8ace |
| institution | OA Journals |
| issn | 2948-1600 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Electronics |
| spelling | doaj-art-7c6e4e315b4b49d08e6252655cfc8ace2025-08-20T02:32:52ZengSpringerDiscover Electronics2948-16002024-11-011111910.1007/s44291-024-00034-xEnd-to-End CNN conceptual model for a biometric authentication mechanism for ATM machinesKarthikeyan Velayuthapandian0Natchiyar Murugan1Saranya Paramasivan2Mepco Schlenk Engineering CollegeMepco Schlenk Engineering CollegeMepco Schlenk Engineering CollegeAbstract People commonly use automated teller machines, also known as ATMs, in contemporary culture. The use of ATMs for currency withdrawal is becoming increasingly widespread in the modern world. Furthermore, attacks and break-ins have increased, highlighting the need for an ATM that is more secure and equipped with extra safety measures. The existing method of confirming these transactions through credit cards and ATMs carries a high level of risk. Card trapping, cash larceny, shoulder surfing, and ATM skimming are among the examples. The proposed system, which is based on biometrics and deep convolutional neural networks, can effectively resolve the problems associated with conventional ATM cards and PINs. The proposed method relies on face biometrics, which offers a novel technique to address the current challenge. This article presents a novel end-to-end multiscale attention-based light-weighted deep convolutional neural network (MA-LW-DCNN) framework for enhancing the accuracy of recognizing faces. The multiscale attention mechanism in face recognition enhances feature extraction by applying convolution filters at various scales, allowing the network to capture detailed and global facial features. It generates an attention map by normalizing characteristic maps and refining them using activation functions, improving recognition accuracy by focusing on essential face regions. The suggested system is assessed using a synthetic dataset against conventional machine and deep learning-based face recognition techniques. The experiment results for different individuals demonstrate an accuracy rate of around 99.84% in authenticating test samples. The proposed approach produced significantly improved evaluation measures precision, recall, and loss by 0.99, 0.99, and 0.16, respectively.https://doi.org/10.1007/s44291-024-00034-xFace authenticationSmart CardUnknown Face Forwarding LinkFace idInternet of things |
| spellingShingle | Karthikeyan Velayuthapandian Natchiyar Murugan Saranya Paramasivan End-to-End CNN conceptual model for a biometric authentication mechanism for ATM machines Discover Electronics Face authentication Smart Card Unknown Face Forwarding Link Face id Internet of things |
| title | End-to-End CNN conceptual model for a biometric authentication mechanism for ATM machines |
| title_full | End-to-End CNN conceptual model for a biometric authentication mechanism for ATM machines |
| title_fullStr | End-to-End CNN conceptual model for a biometric authentication mechanism for ATM machines |
| title_full_unstemmed | End-to-End CNN conceptual model for a biometric authentication mechanism for ATM machines |
| title_short | End-to-End CNN conceptual model for a biometric authentication mechanism for ATM machines |
| title_sort | end to end cnn conceptual model for a biometric authentication mechanism for atm machines |
| topic | Face authentication Smart Card Unknown Face Forwarding Link Face id Internet of things |
| url | https://doi.org/10.1007/s44291-024-00034-x |
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