An Innovative Framework Based Algorithmic Approach for Object Detection

Abstract

Object Detection techniques have been enumerably used in a variety of real-time applications such as robotics, crime investigation, transportation, etc. Object classification is an important task in computer vision and is the process of tagging objects into predefined and semantically significant classes using trained datasets. A framework for object detection using the Viola-Jone algorithm for object classification is proposed in this paper. The architecture of the framework encompasses image acquisition, image pre-processing, classification and extraction of objects, and computation of related measures. The experiment is performed on a dataset containing 120 images of human faces with different angles, poses, and light conditions. It is worth mentioning that the faces as objects are recognized successfully from the set of input images with a rate of 96.67%. Moreover, the objects on faces such as eyes, nose, and mouth are detected successfully with an average accuracy of 93.10%, 93.10%, and 90.80% respectively. The attributes/measures of these objects are vital for computer recognition and hence, the properties/measures of respective objects are computed at the end. This framework will be useful in several real-life applications; especially in criminal investigation applications for identification of persons/criminals and computer portrait designing, etc.