Revealing skull identity through a fusion of Viola-Jones and CCA
Shrutika S. Gayakwad, Prakash S. Mohod · 2015
Face detection using skull has been referred as the most complex and challenging task in the domain of computer vision, by the large intra-class variations occurred due to the changes in facial perspectives, expression and illumination. Distinct approaches have paid attention on this skill, still only open source implementations have been greatly exercised by researchers. The best example is the Viola-Jones Object Detection framework that especially in the case of facial processing has been recurrently used which imparts real-time contentious object detection rates. The important stages stated in this research work is to first eradicate the detected feature parts from face which will be correlated through Canonical Correlation Analysis(CCA) against the feature extracted from the skull. The Canonical correlation analysis is largely concerned with the estimation of a linear composition of each of two pairs of variables suchlike the correlation present between two functions should be maximized. Hence the combination of Viola-Jones with CCA will definitely boost up the matching accuracy as well as ease the task eradication and correlation.