A Novel ROI Extraction Method Based on the Characteristics of the Original Finger Vein Image
As the second generation of biometric technology, finger vein recognition has become a research hotspot due to its advantages such as high security, and living body recognition. In recent years, the global pandemic has promoted the development of contactless identification. However, the unconstraine...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/13/4402 |
| _version_ | 1797411661611532288 |
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| author | Huimin Lu Yifan Wang Ruoran Gao Chengcheng Zhao Yang Li |
| author_facet | Huimin Lu Yifan Wang Ruoran Gao Chengcheng Zhao Yang Li |
| author_sort | Huimin Lu |
| collection | DOAJ |
| description | As the second generation of biometric technology, finger vein recognition has become a research hotspot due to its advantages such as high security, and living body recognition. In recent years, the global pandemic has promoted the development of contactless identification. However, the unconstrained finger vein acquisition process will introduce more uneven illumination, finger image deformation, and some other factors that may affect the recognition, so it puts forward higher requirements for the acquisition speed, accuracy and other performance. Considering the universal, obvious, and stable characteristics of the original finger vein imaging, we proposed a new Region Of Interest (ROI) extraction method based on the characteristics of finger vein image, which contains three innovative elements: a horizontal Sobel operator with additional weights; an edge detection method based on finger contour imaging characteristics; a gradient detection operator based on large receptive field. The proposed methods were evaluated and compared with some representative methods by using four different public datasets of finger veins. The experimental results show that, compared with the existing representative methods, our proposed ROI extraction method is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1</mn><mo>/</mo><mn>10</mn></mrow></semantics></math></inline-formula>th of the processing time of the threshold-based methods, and it is similar to the time spent for coarse extraction in the mask-based methods. The ROI extraction results show that the proposed method has better robustness for different quality images. Moreover, the results of recognition matching experiments on different datasets indicate that our method achieves the best Equal Error Rate (EER) of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.67</mn><mo>%</mo></mrow></semantics></math></inline-formula> without the refinement of feature extraction parameters, and all the EERs are significantly lower than those of the representative methods. |
| first_indexed | 2024-03-09T04:49:23Z |
| format | Article |
| id | doaj.art-f260e6d1b178492ab626d3d9d4d584cf |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:49:23Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f260e6d1b178492ab626d3d9d4d584cf2023-12-03T13:12:17ZengMDPI AGSensors1424-82202021-06-012113440210.3390/s21134402A Novel ROI Extraction Method Based on the Characteristics of the Original Finger Vein ImageHuimin Lu0Yifan Wang1Ruoran Gao2Chengcheng Zhao3Yang Li4School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, ChinaSchool of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, ChinaSchool of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, ChinaSchool of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, ChinaSchool of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, ChinaAs the second generation of biometric technology, finger vein recognition has become a research hotspot due to its advantages such as high security, and living body recognition. In recent years, the global pandemic has promoted the development of contactless identification. However, the unconstrained finger vein acquisition process will introduce more uneven illumination, finger image deformation, and some other factors that may affect the recognition, so it puts forward higher requirements for the acquisition speed, accuracy and other performance. Considering the universal, obvious, and stable characteristics of the original finger vein imaging, we proposed a new Region Of Interest (ROI) extraction method based on the characteristics of finger vein image, which contains three innovative elements: a horizontal Sobel operator with additional weights; an edge detection method based on finger contour imaging characteristics; a gradient detection operator based on large receptive field. The proposed methods were evaluated and compared with some representative methods by using four different public datasets of finger veins. The experimental results show that, compared with the existing representative methods, our proposed ROI extraction method is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1</mn><mo>/</mo><mn>10</mn></mrow></semantics></math></inline-formula>th of the processing time of the threshold-based methods, and it is similar to the time spent for coarse extraction in the mask-based methods. The ROI extraction results show that the proposed method has better robustness for different quality images. Moreover, the results of recognition matching experiments on different datasets indicate that our method achieves the best Equal Error Rate (EER) of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.67</mn><mo>%</mo></mrow></semantics></math></inline-formula> without the refinement of feature extraction parameters, and all the EERs are significantly lower than those of the representative methods.https://www.mdpi.com/1424-8220/21/13/4402biometricsfinger vein recognitionROI extractionidentity authentication |
| spellingShingle | Huimin Lu Yifan Wang Ruoran Gao Chengcheng Zhao Yang Li A Novel ROI Extraction Method Based on the Characteristics of the Original Finger Vein Image Sensors biometrics finger vein recognition ROI extraction identity authentication |
| title | A Novel ROI Extraction Method Based on the Characteristics of the Original Finger Vein Image |
| title_full | A Novel ROI Extraction Method Based on the Characteristics of the Original Finger Vein Image |
| title_fullStr | A Novel ROI Extraction Method Based on the Characteristics of the Original Finger Vein Image |
| title_full_unstemmed | A Novel ROI Extraction Method Based on the Characteristics of the Original Finger Vein Image |
| title_short | A Novel ROI Extraction Method Based on the Characteristics of the Original Finger Vein Image |
| title_sort | novel roi extraction method based on the characteristics of the original finger vein image |
| topic | biometrics finger vein recognition ROI extraction identity authentication |
| url | https://www.mdpi.com/1424-8220/21/13/4402 |
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