Abstract: Driver fatigue is an important factor in the increasing
number of road accidents. Dynamic template matching method was
proposed to address the problem of real-time driver fatigue detection
system based on eye-tracking. An effective vision based approach
was used to analyze the driver’s eye state to detect fatigue. The driver
fatigue system consists of Face detection, Eye detection, Eye
tracking, and Fatigue detection. Initially frames are captured from a
color video in a car dashboard and transformed from RGB into YCbCr
color space to detect the driver’s face. Canny edge operator was used
to estimating the eye region and the locations of eyes are extracted.
The extracted eyes were considered as a template matching for eye
tracking. Edge Map Overlapping (EMO) and Edge Pixel Count
(EPC) matching function were used for eye tracking which is used to
improve the matching accuracy. The pixel of eyeball was tracked
from the eye regions which are used to determine the fatigue state of
the driver.
Abstract: In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined.
Abstract: This paper proposes a hybrid method for eyes localization
in facial images. The novelty is in combining techniques
that utilise colour, edge and illumination cues to improve accuracy.
The method is based on the observation that eye regions have dark
colour, high density of edges and low illumination as compared
to other parts of face. The first step in the method is to extract
connected regions from facial images using colour, edge density and
illumination cues separately. Some of the regions are then removed
by applying rules that are based on the general geometry and shape
of eyes. The remaining connected regions obtained through these
three cues are then combined in a systematic way to enhance the
identification of the candidate regions for the eyes. The geometry
and shape based rules are then applied again to further remove the
false eye regions. The proposed method was tested using images from
the PICS facial images database. The proposed method has 93.7%
and 87% accuracies for initial blobs extraction and final eye detection
respectively.
Abstract: To increase reliability of face recognition system, the
system must be able to distinguish real face from a copy of face such
as a photograph. In this paper, we propose a fast and memory efficient
method of live face detection for embedded face recognition system,
based on the analysis of the movement of the eyes. We detect eyes in
sequential input images and calculate variation of each eye region to
determine whether the input face is a real face or not. Experimental
results show that the proposed approach is competitive and promising
for live face detection.