EyeLSD a Robust Approach for Eye Localization and State Detection

• Type: Article in International Scientific Journal
• Date: 2018

Authors

Benrachou Djamel Eddine (Author) Other; Filipe Neves dos Santos (Author) FEUP; Brahim Boulebtateche (Author) Other; Salah Bensaoula (Author) Other

Journal

Title: Journal of Signal Processing Systems

Vol. 90 No. 1

Pages: 99-125

ISSN: 1939-8018

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00M-DVN

DOI: 10.1007/s11265-016-1219-1

Abstract (EN): Improving the safety of public roads and industrial factories requires more reliable and robust computer vision-based approaches for monitoring the eye state (open or closed) of human operators. Getting this information in real time when humans are driving cars or using hazardous machinery will help to prevent accidents and deaths. This paper proposes a new framework called EyeLSD to localize the eyes and detect their states without face detection step. For EyeLSD aims, two novel descriptors are proposed: enhanced Pyramidal Local Binary Pattern Histogram (ePLBPH) and Multi-Three-Patch LBP histogram (Multi-TPLBP). The performance of EyeLSD with ePLBPH and Multi-TPLBP is evaluated and compared against other approaches. For this evaluation three independent and public datasets were used: BioID, CAS-PEAL-R1 and ZJU datasets. The set EyeLSD, ePLBPH and Multi-TPLBP have a greater performance when compared against the state-of-the-art algorithms. The proposed approach is very stable under large range of eye appearances caused by expression, rotation, lighting, head pose, and occlusion.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 27