Multiple Cascading Algorithms to Evaluate Performance of Face Detection

Abstract

This paper intends to evaluate the previous works done on different cascading classifiers for human face detection of image data. This paper includes the working process, efficiency, and performance comparison of different cascading methods. These methods are dynamic cascade, Haar cascade, SURF cascade, and Fea-Accu cascade. Each cascade classifier is described in this paper with their working procedure and mathematical induction as well. Each technique is backed with proper data and examples. The accuracy rate of the method is given with comparison with analyze the performance of the methods. In this literature, the human face detection process using cascading classifiers from image data is studied. From the study, the performance rate and comparison of different cascading techniques are highlighted. This study will also help to determine which methods are to be used for achieving an accurate accuracy depending on the data and circumstances.