A Study on the Effectiveness of Different Patch Size and Shape for Eyes and Mouth Detection

TL;DR

This paper investigates how different patch sizes and shapes affect the accuracy of eyes and mouth detection using template matching, and evaluates the impact of combining it with grayscale and Haar wavelet transforms.

Contribution

It introduces a method to determine optimal patch size and shape for template matching in facial feature detection and analyzes the effectiveness of combining it with specific image processing techniques.

Findings

Optimal patch size and shape identified for improved detection.

Combining template matching with Haar wavelet transform enhances accuracy.

Grayscale processing also contributes to detection performance.

Abstract

Template matching is one of the simplest methods used for eyes and mouth detection. However, it can be modified and extended to become a powerful tool. Since the patch itself plays a significant role in optimizing detection performance, a study on the influence of patch size and shape is carried out. The optimum patch size and shape is determined using the proposed method. Usually, template matching is also combined with other methods in order to improve detection accuracy. Thus, in this paper, the effectiveness of two image processing methods i.e. grayscale and Haar wavelet transform, when used with template matching are analyzed.