Title: An accurate and fast method for eyelid detection

Authors: Ahmed AK Tahir; Steluta Anghelus

Addresses: Department of Computer Science, College of Science, University of Duhok, Kurdistan Region, Iraq ' Mechanical Department, Technical College "Traian Vuia", Oradea, Romania

Abstract: A novel method called refine-connect-extend-smooth (R-C-E-S) for detecting eyelids is presented. It consists of four algorithms, Canny edge detector with Prewitt operator, modified refine edge map (MREM), connect edges-extend (CEE) and smooth curve (SC). The method is not based on pre-assumptions that consider eyelids as parabola or lines and it does not use curve fitting algorithm, therefore sever deviation of the detected eyelid curve from the actual eyelid path is avoided. The method is applied to three types of database, CASIA-V1.0, CASIA-V4.0-Lamp and SDUMLA-HMT. The accuracies for detecting the lower eyelid, upper eyelid and free iris are (93.2%, 99.1%, 96.7%) for CASIA-V1.0, while for CASIA-V4.0-Lamp are (97.6%, 98.3%, 97.8%) and for SDUMLA-HMT are (95.1%, 95.3%, 96.92%). The processing times for detecting single eyelid, both eyelids and free iris are (42 ms, 49 ms, 35 ms) for CASIA-V1.0, while for CASIA-V4.0-Lamp are (23 ms, 26 ms, 21 ms) and for SDUMLA-HMT are (35 ms, 40 ms, 31 ms).

Keywords: biometics; Canny edge detector; eyelid detection; iris localisation; iris recognition system; Prewitt operator; Sobel operator.

DOI: 10.1504/IJBM.2020.107715

International Journal of Biometrics, 2020 Vol.12 No.2, pp.163 - 178

Received: 12 Mar 2019
Accepted: 24 Jul 2019
Published online: 10 Jun 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article