Detection of Driver's Drowsy Facial Expression

Abstract

We propose a method for the estimation of the degree of a driver's drowsiness on basis of changes in facial expressions captured by an IR camera. Typically, drowsiness is accompanied by falling of eyelids. Therefore, most of the related studies have focused on tracking eyelid movement by monitoring facial feature points. However, textural changes that arise from frowning are also very important and sensitive features in the initial stage of drowsiness, and it is difficult to detect such changes solely using facial feature points. In this paper, we propose a more precise drowsiness-degree estimation method considering wrinkles change by calculating local edge intensity on faces that expresses drowsiness more directly in the initial stage.

Integrate memory mechanism in multi-granularity deep framework for driver drowsiness detection

“…The change of pupil diameter was utilized by Shirakata et al to detect imperceptible drowsiness, which is effective but not convenient for a driver to take the equipment [14] . Nakamura et al utilized face alignment to estimate the degree of drowsiness via K-Nearest Neighbors (k-NN), which cannot achieve online performance [8] . Spatial-temporal features for driver drowsiness detection were proposed by Akrout et al [10] .…”

“…Driver drowsiness is a critical problem that induces 6% of serious road accidents each year [1] . This condition indicates that the driver lacks sleep, which can be detected by the variation of physiological signals [2][3][4][5] , vehicle trajectory [6,7] , and facial expressions [8] . Drowsiness detection using vehicle-based, physiological, and behavioral change measurement systems is possible with embedded pros and cons [9] .…”

“…In particular, the large variation of head pose causes serious deformations of facial shape, which makes it difficult to extract effective spatial representations. Conventional approaches, such as those based on aligned facial points [8] , provide a better way to represent drowsy features; however, their limitation lies in their inability to distinguish between blinking and closing eyes due to the neglect of temporal relationships. Spatial-temporal descriptors [10] are proposed to collect spatial and temporal features, but they are not good at distinguishing states with long-term dependencies, such as yawning and speaking.…”

Driver Drowsiness Classification Based on Eye Blink and Head Movement Features Using the k-NN Algorithm

“…To the best of our knowledge, it is not previously studied in the context of a camera-based driver drowsiness detection using blink features. Existing k-NN-based approaches include the steering behavior [18], EEG measures [21] or facial features [22]. The work of [23] investigates the feasibility of a drowsiness classification system based on blink features gathered with an EOG.…”

Advanced Driver's Alarms System through Multi-agent Paradigm

“…Accordingly, Facial Action Coding System (FACS) [53] widely describes the facial behavior related to diverse emotions, and it is the foundation of some studies to identify the driver's fatigue [54]. Following this research line, Nakamura et al [55] extracted the facial feature points using a face alignment algorithm and applied unsupervised learning to identify the commonest drowsiness face expressions and the transition period towards the fatigue state.…”