Eyelid Detection Method Based on a Fuzzy Multi-Objective Optimization

Yuniol Alvarez Betancourt, Miguel Garcia-Silvente


Iris recognition is one of the most robust human identification methods. In order to carry out accurate iris recognition, many factors of image quality should be born in mind. The eyelid occlusion is a quality factor that may significantly affect the accuracy. In this paper we introduce a new fuzzy multi-objective optimization approach based on the eyelid detection method. This method obtains the eyelid contour which represents the best solution of Pareto-optimal set taking into account five optimized objectives. This proposal is composed of three main stages, namely, gathering eyelid contour information, filtering eyelid contour and tracing eyelid contour. The results of the proposal are evaluated in a verification mode and thus a few performance measures are generated in order to compare them with other works of the state of the art. Thereby, the proposed method outperforms other approaches and it is very useful for implementing real applications as well.


Eyelid detection; eyelid location; iris recognition; fuzzy systems; multi-objective optimization; combinatorial optimization.

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