Modele et dispositifs pour l'evaluation de la charge mentale par la methode de la double tache

SIGLECNRS TD Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

EyeLHM: Real-Time Vision-Based approach for Eye Localization and Head Motion Estimation

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

Online Vision-Based Eye Detection: LBP/SVM vs LBP/LSTM-RNN

Eye detection is a complex issue and widely explored through several applications, such as human gaze detection, human-robot interaction and driver’s drowsiness monitoring. However, most of these applications require an efficient approach for detect the ocular region, which should be able to work in real time. In this paper, it is proposed and compare two approaches for online eye detection. The proposed schemes, work under real variant illumination conditions, using the conventional appearance method that is known for its discriminative power especially in texture analysis.In the first stage, the salient eye features are automatically extracted by employing Uniform Local Binary pattern (LBP) operator. Thereafter, supervised machine learning methods are used to classify the presence of an eye in image path, which is described by an LBP histogram. For this stage, two approaches were tested; Support Vector Machine and Long Short-Term Memory Recurrent Neural Network, both are trained for discriminative binary classification, between two classes namely eye / non eye.The human eyes were successfully localized in real time videos, which were obtained from a laptop with uncalibrated web camera. In these tests, different people were considered and light illumination. The experimental results are reported

EyeLSD a Robust Approach for Eye Localization and State Detection

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