Semantic Gap Reduction From Mouth Feature Threshold Value Using Viola Jones Algorithm

Abstract

Image features are useful in defining image precisely and uniquely which are helpful in classification and recognition of images. Various feature values can be extracted by using different techniques used in image processing applications such as pattern recognition, feature matching, image segmentation, image fusion, video processing, visual surveillance, remote sensing, traffic safety monitoring, medical diagnosis, and human computer interaction etc. Number of factors of image affects on accuracy of feature extraction from different databases of images. The distance of the object (or person) from the camera and threshold value for feature detected plays an important role to capture accurate feature from the image database. The runtime required to capture the feature from image is also considerable factor when an algorithm for feature extraction is selected. The paper considers the distance and threshold value used for mouth feature detection and run-time required to capture the mouth feature from face database using Viola Jones algorithm. The experimental result shows increased threshold value at certain level for mouth feature detection gives better result and improved mouth detection rate for semantic gap reduction.