基于Legendre矩的人脸识别组合分类器

Sridhar Dasari, I. Krishna
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引用次数: 5

摘要

提出了一种基于Legendre矩与线性判别分析和概率神经网络相结合的人脸识别方法。勒让德矩是正交的和尺度不变量的,因此它们适合表示人脸图像的特征。所提出的人脸识别方法包括三个步骤:1)使用Legendre矩提取特征;2)使用线性判别分析(LDA)降维;3)使用概率神经网络(PNN)分类。线性判别分析除降维外,还寻找类的最大判别方向。将勒让德矩与线性判别分析相结合,在图像样本较少的情况下提高线性判别分析的能力。概率神经网络对人脸图像进行了快速、准确的分类。对两个人脸数据库进行了评价。第一个数据库是来自Olivetty研究实验室(ORL)人脸数据库的400张人脸图像,第二个数据库是13名学生的人脸图像。与其他分类器相比,该方法具有更快、更好的识别率。DOI: 10.18495 / comengapp.12.107118
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Combined Classifier for Face Recognition using Legendre Moments
In this paper, a new combined Face Recognition method based on Legendre moments with Linear Discriminant Analysis and Probabilistic Neural Network is proposed. The Legendre moments are orthogonal and scale invariants hence they are suitable for representing the features of the face images. The proposed face recognition method consists of three steps, i) Feature extraction using Legendre moments ii) Dimensionality reduction using Linear Discrminant Analysis (LDA) and iii) classification using Probabilistic Neural Network (PNN). Linear Discriminant Analysis searches the directions for maximum discrimination of classes in addition to dimensionality reduction. Combination of Legendre moments and Linear Discriminant Analysis is used for improving the capability of Linear Discriminant Analysis when few samples of images are available. Probabilistic Neural network gives fast and accurate classification of face images. Evaluation was performed on two face data bases. First database of 400 face images from Olivetty Research Laboratories (ORL) face database, and the second database of thirteen students are taken. The proposed method gives fast and better recognition rate when compared to other classifiers. DOI: 10.18495/comengapp.12.107118
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