Study on Accuracy Improvement of Cat Face Detection

Abstract

We have experimented cat face detection by searching face components such as eye, nose and mouth for cat face candidate regions detected by a classifier constructed with Haar-like feature. In this paper, we describe how to improve the detection accuracy by increasing learning images with their rotation. As the result of the experiments, we have succeeded in detecting cat faces that have rotation, and the precision and the recall were 100% and 88%, respectively.