EyeLHM: Real-Time Vision-based approach for Eye localization and Head motion estimation

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Benrachou, DE (Author) Other; Filipe Neves Santos (Author) FEUP; Boulebtateche, B (Author) Other; Bensaoula, S (Author) Other

Conference proceedings International

Title: 2016 International Conference on Autonomous Robot Systems and Competitions, ICARSC 2016, Bragança, Portugal, May 4-6, 2016

Pages: 305-310

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00M-D6F

DOI: 10.1109/icarsc.2016.39

Abstract (EN): Humans are increasingly cooperating with machinery/robots in a high number of domains and under uncontrolled conditions. When persons are interacting with machinery, they are exposed to distraction/fatigue, which can lead to dangerous situations. The evaluation of individual's attention and fatigue levels is highly needed in such situations. This is an important measurement to avoid the interaction of humans with the machine when these levels of concentration are critical. This paper proposes a real-time vision-based approach for eye localization and head motion estimation (EyeLHM). The proposed method is evaluated under three different databases: GI4E face database, extended Yale-B database and GI4E head pose database. High detection rates are achieved on GI4E head-pose database and face database, 97.35% and 87.19% respectively. EyeLHM approach is optimized to be deployed in low-cost computers, such as RaspberryPi or UDOO Boards.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6