Performance Comparison of Various Features for Human Face Recognition using Machine Learning

K. Swamy, A. Supraja, P. S. Vinuthna, D. L. Sindhura
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Abstract

Facial features are one of the most vital biometrics and are used to identify an individual. Facial recognition is a technology having the capacity to distinguish a specific individual. This technology mainly concentrates on machine learning techniques to learn, acquire, store, and examine facial features to fit them with a database. In this project, the features are extracted in transform domain. Discrete Wavelet Transform (DWT) is applied on images. In transform domain, features like mean, energy, Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), Gabor Filters are explored. Appropriate features are extracted from LL, LH, HL, and HH bands to build a machine learning model. Database is divided into training and testing. Model is built based on the 80% of images from the database. Model is tested with 20% images of test data. Three important machine learning algorithms are popular. These are Decision Tree (DT), Support Vector Machines and Naive Bayes (NB). NB is used for probability-based inferences. DT is simpler than SVM. Hence, Decision Tree-based Machine Learning is employed to recognize the faces. Accuracy is used to test the performance of various features.
使用机器学习进行人脸识别的各种特征的性能比较
面部特征是最重要的生物特征之一,用于识别个体。面部识别是一种能够区分特定个体的技术。该技术主要集中于机器学习技术,以学习,获取,存储和检查面部特征,以使其与数据库相匹配。在本项目中,特征提取是在变换域中进行的。对图像进行离散小波变换(DWT)。在变换域,研究了均值、能量、定向梯度直方图(HOG)、局部二值模式(LBP)、Gabor滤波器等特征。从LL、LH、HL和HH波段中提取适当的特征,构建机器学习模型。数据库分为训练和测试两部分。基于数据库中80%的图像构建模型。用20%的测试数据图像对模型进行测试。有三种重要的机器学习算法很流行。这些是决策树(DT),支持向量机和朴素贝叶斯(NB)。NB用于基于概率的推断。DT比SVM简单。因此,基于决策树的机器学习被用于人脸识别。精度是用来测试各种特征的性能。
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