Abstract
The concept of cohort selection has been emerged as a very interesting and potential topic for ongoing research in biometrics. It has the capability to provide the traditional biometric systems to having a higher performance rate with lesser complexity and cost. This paper describes a novel matching technique incorporated with Bezier curve cohort selection. The Best-Fit matching with dynamic threshold has been proposed here to reduce the number of false match. This algorithm is applied for matching of Speeded Up Robust Feature (SURF) points detected on face images to find out the matching score between two faces. After that, Bezier curve is applied as a cohort selection technique. All the cohort scores are plotted in a 2D plane as if these are the control points of a Bezier curve and then a Bezier curve of degree n is plotted on the same plane using De Casteljau algorithm where number of control point is \(n+1\). A template contains more discriminative features more it is having distance from the curve. All the templates having score point far from the curve are included into the account of cohort subset. For each enrolled user a specific cohort subset is determined. As long as the subset is formed, T-norm cohort score normalization technique is applied to obtain the normalized scores which are further used for person identification and verification. Experiments are conducted on FEI face database and results are showing dominance over the non-cohort system.
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Garain, J., Shah, A., Kumar, R.K., Kisku, D.R., Sanyal, G. (2017). BCP-BCS: Best-Fit Cascaded Matching Paradigm with Cohort Selection Using Bezier Curve for Individual Recognition. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10118. Springer, Cham. https://doi.org/10.1007/978-3-319-54526-4_28
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DOI: https://doi.org/10.1007/978-3-319-54526-4_28
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