CASIA-Iris-Africa: A Large-scale African Iris Image Database

doi:10.1007/s11633-022-1402-8

CASIA OpenIR > 学术期刊 > Machine Intelligence Research

	CASIA-Iris-Africa: A Large-scale African Iris Image Database
	Jawad Muhammad 1,2; Yunlong Wang1,2 ; Junxing Hu1,2 ; Kunbo Zhang1,2 ; Zhenan Sun1,2
发表期刊	Machine Intelligence Research
ISSN	2731-538X
	2024
卷号	21 期号:2 页码:383-399
摘要	Iris biometrics is a phenotypic biometric trait that has proven to be agnostic to human natural physiological changes. Research on iris biometrics has progressed tremendously, partly due to publicly available iris databases. Various databases have been available to researchers that address pressing iris biometric challenges such as constraint, mobile, multispectral, synthetics, long-distance, contact lenses, liveness detection, etc. However, these databases mostly contain subjects of Caucasian and Asian docents with very few Africans. Despite many investigative studies on racial bias in face biometrics, very few studies on iris biometrics have been published, mainly due to the lack of racially diverse large-scale databases containing sufficient iris samples of Africans in the public domain. Furthermore, most of these databases contain a relatively small number of subjects and labelled images. This paper proposes a large-scale African database named Chinese Academy of Sciences Institute of Automation (CASIA)-Iris-Africa that can be used as a complementary database for the iris recognition community to mediate the effect of racial biases on Africans. The database contains 28 717 images of 1023 African subjects (2 046 iris classes) with age, gender, and ethnicity attributes that can be useful in demographically sensitive studies of Africans. Sets of specific application protocols are incorporated with the database to ensure the database′s variability and scalability. Performance results of some open-source state-of-the-art (SOTA) algorithms on the database are presented, which will serve as baseline performances. The relatively poor performances of the baseline algorithms on the proposed database despite better performance on other databases prove that racial biases exist in these iris recognition algorithms. The database will be made available on our website: http://www.idealtest.org.
关键词	African iris recognition, racial bias, iris image database, biometrics, iris recognition
DOI	10.1007/s11633-022-1402-8
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/56045
专题	学术期刊_Machine Intelligence Research
作者单位	1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 2.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Jawad Muhammad,Yunlong Wang,Junxing Hu,et al. CASIA-Iris-Africa: A Large-scale African Iris Image Database[J]. Machine Intelligence Research,2024,21(2):383-399.
APA	Jawad Muhammad,Yunlong Wang,Junxing Hu,Kunbo Zhang,&Zhenan Sun.(2024).CASIA-Iris-Africa: A Large-scale African Iris Image Database.Machine Intelligence Research,21(2),383-399.
MLA	Jawad Muhammad,et al."CASIA-Iris-Africa: A Large-scale African Iris Image Database".Machine Intelligence Research 21.2(2024):383-399.

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