Eye Detection Using Composite Cross-Correlation

This study presents a new eye detection method depending on composite template matching for facial images. The objective of this study is to utilize template match method to detect the eyes from given images and to improve this method to obtain higher rate of detection. The idea of our method is to integrate cross correlations of various eye templates. Thus, the correct values of single template matching based eye detection dominated the final output. It also contributed to the re-correct the detection in the event of failure of all single templates. The study also presents a method to obtain candidate eye pixels which contribute to abbreviate the time required to implement up to 91%. The formula of composite cross correlation has been generalized taking into account the differences between the sizes, shifts and irregular single templates. The experiments applied on PICS database reported 98.76% as eye detection rate.