EyeLSD a Robust Approach for Eye Localization and State Detection

• Tipo: Artigo em Revista Científica Internacional
• Data: 2018

Autores

Benrachou Djamel Eddine (Autor) Outra; Filipe Neves dos Santos (Autor) FEUP; Brahim Boulebtateche (Autor) Outra; Salah Bensaoula (Autor) Outra

Revista

Título: Journal of Signal Processing Systems

Vol. 90 Nº 1

Páginas: 99-125

ISSN: 1939-8018

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 5 Citações

Scopus - 15 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 27