An Enhanced Eye-Tracking Approach Using Pipeline Computation

Tracking and authentication at a low cost and in a convenient way is an important issue nowadays as opposed to using high cost for specified hardware and complex calibration process. In the paper, the prime concentration is divided into two: (i) eye tracking and (ii) parallelism in execution. First part has been subdivided as: Firstly, human face is taken from video sequence, and this is divided into frames. Eye regions are recognized from image frames. Secondly, iris region and pupils (central point) are being identified. In identifying pupil, the energy intensity and edge strength are taken into account together. Iris and eye corners are considered as tracking points. The famous sinusoidal head model is used for 3-D head shape, and adaptive probability density function (APDF) is proposed for estimating pose in facial features extraction. Thirdly, iris (pupil) tracking is completed by integrating eye vector and head movement information gained from APDF. The second part focuses on reducing execution time (ET) by applying pipeline architecture (PA). So the entire process has been subdivided into three phases. Phase-I is tracking eyes and pupils and preparing an eye vector. Phase-II is completing the calibration process. Phase-III is for matching and feature extraction. PA is integrated to improve performance in terms of ET because three phases are running in parallel. The experiment is done based on CK + image sets. Indeed, by using PA, the ET is reduced by (66.68%) two-thirds. The average accuracy in tracking (91% in left eye pupil (LEP)) and 93.6% in right eye pupil (REP)) is a robust one.