Title :
A Supervised Fuzzy Eye Pair Detection Algorithm
Author :
Susan, Seba ; Kadyan, Pooja
Author_Institution :
Dept. of Inf. Technol., Delhi Technol. Univ., New Delhi, India
Abstract :
Most of the face recognition problems in literature rely on automatic eye-pair detection algorithms for locating the eye positions followed by the normalization of the face image based on the distance between the eyes. In this paper we propose a supervised fuzzy eye pair detection algorithm that can be executed in real time and requires minimal training. Nine categories of facial geometrical measurements are defined. The Gaussian function is used to compute the fuzzy memberships with the mean and standard deviation of the Gaussian being evaluated from ten reference images from the database chosen randomly. The eye pair detection algorithm works successfully on the Utrecht face database except for two cases where the eyebrow pairs are detected. The results are shown to outperform the popular eye variance filter method for eye detection.
Keywords :
Gaussian processes; face recognition; fuzzy set theory; geometry; Gaussian function; Utrecht face database except; eye positions; eye variance filter method; eyebrow pairs; face image normalization; face recognition problems; facial geometrical measurements; fuzzy memberships; mean deviation; reference images; standard deviation; supervised fuzzy eye pair detection algorithm; Detection algorithms; Face; Facial features; Feature extraction; Filtering algorithms; Image segmentation; Pattern recognition; Automatic Eye pair detection; Fuzzy Membership functions; Gaussian membership function; Supervised eye detection;
Conference_Titel :
Computational Intelligence and Communication Networks (CICN), 2013 5th International Conference on
Conference_Location :
Mathura
DOI :
10.1109/CICN.2013.70
