Real-Time Embedded Eye Image Defocus Estimation for Iris Biometrics
One of the main challenges faced by iris recognition systems is to be able to work with people in motion, where the sensor is at an increasing distance (more than 1 m) from the person. The ultimate goal is to make the system less and less intrusive and require less cooperation from the person. When...
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/17/7491 |
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| author | Camilo A. Ruiz-Beltrán Adrián Romero-Garcés Martín González-García Rebeca Marfil Antonio Bandera |
| author_facet | Camilo A. Ruiz-Beltrán Adrián Romero-Garcés Martín González-García Rebeca Marfil Antonio Bandera |
| author_sort | Camilo A. Ruiz-Beltrán |
| collection | DOAJ |
| description | One of the main challenges faced by iris recognition systems is to be able to work with people in motion, where the sensor is at an increasing distance (more than 1 m) from the person. The ultimate goal is to make the system less and less intrusive and require less cooperation from the person. When this scenario is implemented using a single static sensor, it will be necessary for the sensor to have a wide field of view and for the system to process a large number of frames per second (fps). In such a scenario, many of the captured eye images will not have adequate quality (contrast or resolution). This paper describes the implementation in an MPSoC (multiprocessor system-on-chip) of an eye image detection system that integrates, in the programmable logic (PL) part, a functional block to evaluate the level of defocus blur of the captured images. In this way, the system will be able to discard images that do not have the required focus quality in the subsequent processing steps. The proposals were successfully designed using Vitis High Level Synthesis (VHLS) and integrated into an eye detection framework capable of processing over 57 fps working with a 16 Mpixel sensor. Using, for validation, an extended version of the CASIA-Iris-distance V4 database, the experimental evaluation shows that the proposed framework is able to successfully discard unfocused eye images. But what is more relevant is that, in a real implementation, this proposal allows discarding up to 97% of out-of-focus eye images, which will not have to be processed by the segmentation and normalised iris pattern extraction blocks. |
| first_indexed | 2024-03-10T23:13:35Z |
| format | Article |
| id | doaj.art-99a5788a8e6846e296eeb255c73b6cb0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:13:35Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-99a5788a8e6846e296eeb255c73b6cb02023-11-19T08:50:35ZengMDPI AGSensors1424-82202023-08-012317749110.3390/s23177491Real-Time Embedded Eye Image Defocus Estimation for Iris BiometricsCamilo A. Ruiz-Beltrán0Adrián Romero-Garcés1Martín González-García2Rebeca Marfil3Antonio Bandera4Departamento Tecnologia Electronica, ETSI Telecomunicacion, University of Málaga, 29071 Málaga, SpainDepartamento Tecnologia Electronica, ETSI Telecomunicacion, University of Málaga, 29071 Málaga, SpainDepartamento Tecnologia Electronica, ETSI Telecomunicacion, University of Málaga, 29071 Málaga, SpainDepartamento Tecnologia Electronica, ETSI Telecomunicacion, University of Málaga, 29071 Málaga, SpainDepartamento Tecnologia Electronica, ETSI Telecomunicacion, University of Málaga, 29071 Málaga, SpainOne of the main challenges faced by iris recognition systems is to be able to work with people in motion, where the sensor is at an increasing distance (more than 1 m) from the person. The ultimate goal is to make the system less and less intrusive and require less cooperation from the person. When this scenario is implemented using a single static sensor, it will be necessary for the sensor to have a wide field of view and for the system to process a large number of frames per second (fps). In such a scenario, many of the captured eye images will not have adequate quality (contrast or resolution). This paper describes the implementation in an MPSoC (multiprocessor system-on-chip) of an eye image detection system that integrates, in the programmable logic (PL) part, a functional block to evaluate the level of defocus blur of the captured images. In this way, the system will be able to discard images that do not have the required focus quality in the subsequent processing steps. The proposals were successfully designed using Vitis High Level Synthesis (VHLS) and integrated into an eye detection framework capable of processing over 57 fps working with a 16 Mpixel sensor. Using, for validation, an extended version of the CASIA-Iris-distance V4 database, the experimental evaluation shows that the proposed framework is able to successfully discard unfocused eye images. But what is more relevant is that, in a real implementation, this proposal allows discarding up to 97% of out-of-focus eye images, which will not have to be processed by the segmentation and normalised iris pattern extraction blocks.https://www.mdpi.com/1424-8220/23/17/7491eye detectionHaar-like featuresconvolution kernelsdefocus testUltrascale+ MP SoC |
| spellingShingle | Camilo A. Ruiz-Beltrán Adrián Romero-Garcés Martín González-García Rebeca Marfil Antonio Bandera Real-Time Embedded Eye Image Defocus Estimation for Iris Biometrics Sensors eye detection Haar-like features convolution kernels defocus test Ultrascale+ MP SoC |
| title | Real-Time Embedded Eye Image Defocus Estimation for Iris Biometrics |
| title_full | Real-Time Embedded Eye Image Defocus Estimation for Iris Biometrics |
| title_fullStr | Real-Time Embedded Eye Image Defocus Estimation for Iris Biometrics |
| title_full_unstemmed | Real-Time Embedded Eye Image Defocus Estimation for Iris Biometrics |
| title_short | Real-Time Embedded Eye Image Defocus Estimation for Iris Biometrics |
| title_sort | real time embedded eye image defocus estimation for iris biometrics |
| topic | eye detection Haar-like features convolution kernels defocus test Ultrascale+ MP SoC |
| url | https://www.mdpi.com/1424-8220/23/17/7491 |
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