  This thesis describes the development of fast algorithms for the computation
of PERcentage CLOSure of eyes (PERCLOS) and Saccadic Ratio (SR). PERCLOS and SR
are two ocular parameters reported to be measures of alertness levels in human
beings. PERCLOS is the percentage of time in which at least 80% of the eyelid
remains closed over the pupil. Saccades are fast and simultaneous movement of
both the eyes in the same direction. SR is the ratio of peak saccadic velocity
to the saccadic duration. This thesis addresses the issues of image based
estimation of PERCLOS and SR, prevailing in the literature such as illumination
variation, poor illumination conditions, head rotations etc. In this work,
algorithms for real-time PERCLOS computation has been developed and implemented
on an embedded platform. The platform has been used as a case study for
assessment of loss of attention in automotive drivers. The SR estimation has
been carried out offline as real-time implementation requires high frame rates
of processing which is difficult to achieve due to hardware limitations. The
accuracy in estimation of the loss of attention using PERCLOS and SR has been
validated using brain signals, which are reported to be an authentic cue for
estimating the state of alertness in human beings. The major contributions of
this thesis include database creation, design and implementation of fast
algorithms for estimating PERCLOS and SR on embedded computing platforms.
