Author:
Hashim Siti,Mccullagh Paul
Abstract
the Haar Cascade Classifier is a popular technique for object detection that uses a machine-learning approach to identify objects in images and videos. In the context of face detection, the algorithm uses a series of classifiers that are trained on thousands of positive and negative images to identify regions of the image that may contain a face. The algorithm is a multi-stage process that involves collecting training data, extracting features, training the classifiers, building the cascade classifier, detecting faces in the test image, and post-processing the results to remove false positives and false negatives. The algorithm has been shown to be highly accurate and efficient for detecting faces in images and videos, but it has some limitations, including difficulty in detecting faces under challenging lighting conditions or when the faces are partially occluded. Overall, the Haar Cascade Classifier algorithm remains a powerful and widely-used tool for face detection, but it is important to carefully evaluate its performance in the specific context of each application and consider using more advanced techniques when necessary.
Subject
Industrial and Manufacturing Engineering,Materials Science (miscellaneous),Business and International Management
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. An Efficient System for Personal Information Search in Cyberspace Using Facial Recognition Technology;2023 12th International Conference on Control, Automation and Information Sciences (ICCAIS);2023-11-27
2. Driver Fatigue Countermeasure System;2023 4th IEEE Global Conference for Advancement in Technology (GCAT);2023-10-06