Eyelid Detection Method Based on a Fuzzy Multi-Objective Optimization

Método de detección de parpados basado en un enfoque difuso de optimización multiobjetivo

Yuniol Alvarez-Betancourt¹ and Miguel Garcia-Silvente²

¹ Department of Computer Sciences, University of Cienfuegos, Cuba. yalvarezb@ucf.edu.cu

Abstract

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.

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

Resumen

El reconocimiento del iris es considerado como uno de los métodos más robustos de identificación de humanos. Para realizar el reconocimiento con precisión se deben tener en cuenta varios factores de calidad de la imagen. La oclusión del párpado es un factor de calidad que afecta significativamente la precisión. En este artículo se presenta un nuevo método para detectar las oclusiones del párpado basado en un enfoque difuso de optimización con múltiples objetivos. Este método está compuesto por tres etapas principales: recopilación de información, filtrado y trazado del contorno del párpado. Los resultados del método propuesto son evaluados en un esquema de verificación y de esta forma se estiman algunas medidas de desempeño que son comparadas con otros trabajos del estado del arte. El método propuesto supera otros enfoques propuestos y resulta muy útil en la implementación de aplicaciones reales.

Palabras clave. Detección de párpados, localización de párpados, reconocimiento del iris, sistemas difusos, optimización multiobjetivo, optimización combinatorial.

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