Biometric recognition model using deep convolutional neural networks and computer vision
Authentication of the person by means of unique features such as finger veins is used in various fields such as security.In this research, a verification method based on convolutional neural networks with the help of computer vision is proposed.Through experimentation, it was possible to create an a...
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Ponencia |
| Lenguaje: | inglés |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | Enlace permanente (DOI o Handle) Descargar el archivo Enlace permanente (DOI o Handle) |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1881700463220359168 |
|---|---|
| author | Christian Ovalle Daniel Sumire Qquenta Julian Nestor Vilca Sucapuca Rebeca Sumire Qquenta |
| author_browse | Christian Ovalle Daniel Sumire Qquenta Julian Nestor Vilca Sucapuca Rebeca Sumire Qquenta |
| author_facet | Christian Ovalle Daniel Sumire Qquenta Julian Nestor Vilca Sucapuca Rebeca Sumire Qquenta |
| author_sort | Christian Ovalle |
| collection | Producción científica |
| description | Authentication of the person by means of unique features such as finger veins is used in various fields such as security.In this research, a verification method based on convolutional neural networks with the help of computer vision is proposed.Through experimentation, it was possible to create an artificial intelligence model that shows the measurements of loss and precision when verifying the images of a data set.Finally, it is concluded that the loss of biometric recognition, the lower the percentage, the better the model performs.For the modified VGG16 model, 40 epochs were carried out for training, while the Mobilenet was 50 epochs.Additionally, at the end of the execution, the proposed architecture finished in 13 minutes and 45 minutes. |
| format | Conference paper |
| id | openalex:W4385864641 |
| institution | UPeU |
| language | English |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| record_format | dspace |
| spelling | openalex:W4385864641Biometric recognition model using deep convolutional neural networks and computer visionChristian OvalleDaniel Sumire QquentaJulian Nestor Vilca SucapucaRebeca Sumire Qquentabiometric identificationfinger vein recognitionuser authenticationimage verificationconvolutional neural networksVGG16MobileNetmodel training timeBiometric Identification and SecurityFace recognition and analysisUser Authentication and Security SystemsAuthentication of the person by means of unique features such as finger veins is used in various fields such as security.In this research, a verification method based on convolutional neural networks with the help of computer vision is proposed.Through experimentation, it was possible to create an artificial intelligence model that shows the measurements of loss and precision when verifying the images of a data set.Finally, it is concluded that the loss of biometric recognition, the lower the percentage, the better the model performs.For the modified VGG16 model, 40 epochs were carried out for training, while the Mobilenet was 50 epochs.Additionally, at the end of the execution, the proposed architecture finished in 13 minutes and 45 minutes.Conference paperhttps://doi.org/10.18687/laccei2023.1.1.492https://laccei.org/LACCEI2023-BuenosAires/papers/Contribution_492_a.pdfhttp://dx.doi.org/10.18687/laccei2023.1.1.492W4385864641enLACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI)2023-01-01 |
| spellingShingle | biometric identification finger vein recognition user authentication image verification convolutional neural networks VGG16 MobileNet model training time Biometric Identification and Security Face recognition and analysis User Authentication and Security Systems Christian Ovalle Daniel Sumire Qquenta Julian Nestor Vilca Sucapuca Rebeca Sumire Qquenta Biometric recognition model using deep convolutional neural networks and computer vision |
| title | Biometric recognition model using deep convolutional neural networks and computer vision |
| title_full | Biometric recognition model using deep convolutional neural networks and computer vision |
| title_fullStr | Biometric recognition model using deep convolutional neural networks and computer vision |
| title_full_unstemmed | Biometric recognition model using deep convolutional neural networks and computer vision |
| title_short | Biometric recognition model using deep convolutional neural networks and computer vision |
| title_sort | biometric recognition model using deep convolutional neural networks and computer vision |
| topic | biometric identification finger vein recognition user authentication image verification convolutional neural networks VGG16 MobileNet model training time Biometric Identification and Security Face recognition and analysis User Authentication and Security Systems |
| topic_facet | biometric identification finger vein recognition user authentication image verification convolutional neural networks VGG16 MobileNet model training time Biometric Identification and Security Face recognition and analysis User Authentication and Security Systems |
| url | https://doi.org/10.18687/laccei2023.1.1.492 https://laccei.org/LACCEI2023-BuenosAires/papers/Contribution_492_a.pdf http://dx.doi.org/10.18687/laccei2023.1.1.492 |
| work_keys_str_mv | AT christianovalle biometricrecognitionmodelusingdeepconvolutionalneuralnetworksandcomputervision AT danielsumireqquenta biometricrecognitionmodelusingdeepconvolutionalneuralnetworksandcomputervision AT juliannestorvilcasucapuca biometricrecognitionmodelusingdeepconvolutionalneuralnetworksandcomputervision AT rebecasumireqquenta biometricrecognitionmodelusingdeepconvolutionalneuralnetworksandcomputervision |