Examples - Driver Drowsiness Detection: Systems and Solutions - page 40

Several papers recognize the complexity of building the whole driver's
drowsiness detection system and in return only focus on significant modules of
the system. One example of such type of effort is a recent paper by Qin, Liu
and Hong [ 14 ]. They strictly focus on eye state detection and identification while
assuming that other modules, such as face and eye detection, are given. They
propose building an eye model based on the Embedded Hidden Markov Model
(EHMM) using color frequency features of images containing closed and open eyes
extracted by applying two-dimensional Discrete Cosine Transformation (2-D DCT)
on them. The low frequency coefficients are used to generate EHMM observation
vectors that are then used to train the EHMM model of an eye.
Lien and Lin [ 9 ] follow the most commonly used approach of face and eye
detection, feature extraction and eye state analysis based on different feature set
for different eye state. In this paper they propose the computation of least correlated
local binary patterns (LBP), which are used to create highly discriminate image
features that can be used for robust eye state recognition. An additional novelty
of the proposed method is the use of independent component analysis (ICA) in
order to derive statistically independent and low dimensional feature vectors. The
feature vectors are used as classification data for an SVM classifier, which provides
information about the current eye state. This information is used to determine the
frequency of blinking, which is then used to determine the state of the driver.
Lenskiy and Lee [ 7 ] propose a sophisticated system that monitors and measures
the frequency of blinking as well as the duration of the eye being closed. In order to
detect the eyes, a skin color segmentation algorithm with facial feature segmentation
algorithm is used. For skin color segmentation, a neural network is trained by using
RGB skin color histogram. Different facial regions, such as eyes, cheeks, nose,
mouth etc. can be considered as different texture regions which can be used to sub-
segment given skin model regions of interest. Each segment is filtered additionally
to create SURF features that are used to estimate each class's probability density
function (PDF). The segmented eye region is filtered with Circular Hough transform
in order to locate iris candidates, i.e., the location of the driver's eyes. To track the
eyes over an extended period of time, Kalman filtering is used.

5.2 Our Work

This section describes the requirements, constraints, basic architecture, and selected
algorithms associated with a driver drowsiness detection system currently being
developed by the authors. It consists of four stages:

1. System Initialization—Preparation
2. Regular Stage—Eye Tracking with Eye-State Analysis
3. Warning Stage—Nod Analysis
4. Alert Stage