Abstract
A uni-biometric system suffers from unbalanced accuracy because of image quality, features extraction weakness, matching algorithm and limited degrees of freedom. This can be overcome by using multiple evidences of the same identity (Multi-biometrics fusion). In a previous work, we proposed new fusion functions based on arithmetic operators and search the best ones using Genetic Programming on the XM2VTS score database. The objective function is based on the Half Total Error Rate (HTER) (a threshold dependent metrics), from the Expected Performance Curve (EPC), of fused matching scores. In this paper, we select ten functions from the generated ones and apply them on matching scores of different biometric systems, which are provided by the bio-secure database. This database provide 24 streams that we use to generate 1000 multi-biometric combinations that we, then, use to conduct our comparative study. Since the result of fusion can be biased and requires a good quality assessment to evaluate the degree of reliability of a processed scheme, we use quality weights on the proposed functions and we compare the results with existing approaches. The proposed quality weights help to reduce the Equal Error Rate (EER a threshold-independent metric) since the obtained matching scores are results of different fusions of instances, sensors and evidences. The EER range is optimized along the tested functions. To confirm that our proposed functions give better score results than the existing functions based on arithmetic rules, we perform multiple statistical significance tests to check the reliability of our experimentation.
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Artabaz, S., Sliman, L. (2019). Study of Score Fusion and Quality Weighting in the Bio-Secure DS2 Database. In: Berry, M., Yap, B., Mohamed, A., Köppen, M. (eds) Soft Computing in Data Science. SCDS 2019. Communications in Computer and Information Science, vol 1100. Springer, Singapore. https://doi.org/10.1007/978-981-15-0399-3_20
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