A Detailed Study and Analysis on Different Machine Learning Solutions Available in the Field of Face Tracking

Aditya M. Gaikwad, M. Gopani, Rutvij Mehta, Sudhir Dhage
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Abstract

Face tracking in today's age is a lesser explored field with a lot of potential to improve security in the society. The aim of this paper is to study and compare different machine learning techniques used for tracking faces in a video. We have encompassed various algorithms in our study, namely, Kalman filtering, Fuzzy geometric set model, Principal Component Analysis, etc.
人脸跟踪领域中不同机器学习解决方案的详细研究与分析
在当今时代,人脸追踪是一个较少被探索的领域,但它有很大的潜力来提高社会的安全性。本文的目的是研究和比较用于跟踪视频中人脸的不同机器学习技术。我们的研究涵盖了各种算法,即卡尔曼滤波,模糊几何集模型,主成分分析等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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