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Assessing the quality of acquired images to improve ear recognition for children

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dc.contributor.author Ntshangase, Cynthia S
dc.contributor.author Ndlovu, Lungisani
dc.contributor.author Stofile, Akhona
dc.date.accessioned 2024-07-12T10:31:06Z
dc.date.available 2024-07-12T10:31:06Z
dc.date.issued 2023-06
dc.identifier.citation Ntshangase, C.S., Ndlovu, L. & Stofile, A. 2023. Assessing the quality of acquired images to improve ear recognition for children. <i>Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 499.</i> http://hdl.handle.net/10204/13714 en_ZA
dc.identifier.issn 1867-8211
dc.identifier.issn 1867-822X
dc.identifier.uri https://doi.org/10.1007/978-3-031-34896-9_22
dc.identifier.uri http://hdl.handle.net/10204/13714
dc.description.abstract The use of biometrics to secure the identity of children is a continuous research worldwide. In the recent past, it has been realized that one of the promising biometrics is the shape of the ear, especially for children. This is be cause most of their biometrics change as they grow. However, there are shortcomings involved when using ear recognition in children, usually caused by the surrounding environment, and children can be at times uncooperative, such as moving during image acquisition. Consequently, the quality of acquired images might be affected by issues such as partial occlusions, blurriness, sharpness, and illumination. Therefore, in this paper, a method of image quality assessment is proposed. This method detects whether the images are affected by partial occlusions, blurriness, sharpness, or illumination. This method assesses the quality of the image to improve ear recognition for children. In this paper, four different test experiments were performed using the AIM database, IIT DELHI ear database, and ear images collected by Council for Scientific and Industrial Research (CSIR) researchers. The Gabor filter and Scale Invariant Feature Transform (SIFT) feature comparison methods were used to assess the quality of images. The experimental results showed that partial ear occlusions has less than 16 key points, resulting in low identification accuracy. Meanwhile, blurriness and sharpness were measured using the sharpness value of the image. Therefore, if the sharpness value is below 13, it means that the image is blurry. On the other hand, if the sharpness value is greater than 110, the image quality affects the ex tracted features and reduces the identification accuracy. Furthermore, it was discovered that the level of illumination in the image varies, the higher the illumination effect, such as the value above 100 affects the features and reduces the identification rate. The overall experimental evaluations demonstrated that image quality assessment is critical in improving ear recognition accuracy. en_US
dc.format Fulltext en_US
dc.language.iso en en_US
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-031-34896-9_22 en_US
dc.source Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 499 en_US
dc.subject Biometrics en_US
dc.subject Children's identity en_US
dc.subject Image quality en_US
dc.subject Ear recognition en_US
dc.title Assessing the quality of acquired images to improve ear recognition for children en_US
dc.type Article en_US
dc.description.pages 369–380 en_US
dc.description.note This is the preprint version of the work. en_US
dc.description.cluster Defence and Security en_US
dc.description.impactarea Inf and Cybersecurity Centre en_US
dc.identifier.apacitation Ntshangase, C. S., Ndlovu, L., & Stofile, A. (2023). Assessing the quality of acquired images to improve ear recognition for children. <i>Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 499</i>, http://hdl.handle.net/10204/13714 en_ZA
dc.identifier.chicagocitation Ntshangase, Cynthia S, Lungisani Ndlovu, and Akhona Stofile "Assessing the quality of acquired images to improve ear recognition for children." <i>Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 499</i> (2023) http://hdl.handle.net/10204/13714 en_ZA
dc.identifier.vancouvercitation Ntshangase CS, Ndlovu L, Stofile A. Assessing the quality of acquired images to improve ear recognition for children. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 499. 2023; http://hdl.handle.net/10204/13714. en_ZA
dc.identifier.ris TY - Article AU - Ntshangase, Cynthia S AU - Ndlovu, Lungisani AU - Stofile, Akhona AB - The use of biometrics to secure the identity of children is a continuous research worldwide. In the recent past, it has been realized that one of the promising biometrics is the shape of the ear, especially for children. This is be cause most of their biometrics change as they grow. However, there are shortcomings involved when using ear recognition in children, usually caused by the surrounding environment, and children can be at times uncooperative, such as moving during image acquisition. Consequently, the quality of acquired images might be affected by issues such as partial occlusions, blurriness, sharpness, and illumination. Therefore, in this paper, a method of image quality assessment is proposed. This method detects whether the images are affected by partial occlusions, blurriness, sharpness, or illumination. This method assesses the quality of the image to improve ear recognition for children. In this paper, four different test experiments were performed using the AIM database, IIT DELHI ear database, and ear images collected by Council for Scientific and Industrial Research (CSIR) researchers. The Gabor filter and Scale Invariant Feature Transform (SIFT) feature comparison methods were used to assess the quality of images. The experimental results showed that partial ear occlusions has less than 16 key points, resulting in low identification accuracy. Meanwhile, blurriness and sharpness were measured using the sharpness value of the image. Therefore, if the sharpness value is below 13, it means that the image is blurry. On the other hand, if the sharpness value is greater than 110, the image quality affects the ex tracted features and reduces the identification accuracy. Furthermore, it was discovered that the level of illumination in the image varies, the higher the illumination effect, such as the value above 100 affects the features and reduces the identification rate. The overall experimental evaluations demonstrated that image quality assessment is critical in improving ear recognition accuracy. DA - 2023-06 DB - ResearchSpace DP - CSIR J1 - Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 499 KW - Biometrics KW - Children's identity KW - Image quality KW - Ear recognition LK - https://researchspace.csir.co.za PY - 2023 SM - 1867-8211 SM - 1867-822X T1 - Assessing the quality of acquired images to improve ear recognition for children TI - Assessing the quality of acquired images to improve ear recognition for children UR - http://hdl.handle.net/10204/13714 ER - en_ZA
dc.identifier.worklist 26267 en_US


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