PCA与LTP特征提取方法的比较及降维对Bat算法人脸识别的影响

Azita Mousavi, Hadis Arefanjazi, Mona Sadeghi, Ali Mojarrad Ghahfarokhi, Fatemehalsadat Beheshtinejad, Mahsa Madadi Masouleh
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引用次数: 0

摘要

人脸识别是图像处理中具有挑战性的课题之一。面部识别通常是一种生物识别方法,基本上是用脸来识别人。人脸识别系统主要包括三个步骤:在图像中找到人脸、特征提取和分类。人脸识别系统面临着光照变化、年龄变化、面部表情变化等挑战。该系统的一个重要问题是算法的执行速度。为此,特征向量的维度应该足够小,特别是当数据库较大时。由于人脸识别系统必须在广泛的数据库上执行,因此需要降维技术来减少时间并提高准确性。降维方法用于此目的。本文给出了两种降维方法:LTP和PCA。在本研究中,首先从人脸图像中提取LTP特征向量,然后使用Bat算法选择有效特征。因此,该算法主要分为特征提取、特征选择和分类三个阶段。该算法是在ORL数据库上实现的,该数据库包含40个不同的人的400张图像,大小为112×92像素。除了减少测试所需的时间外,所提出的方法还提供了99%的非常好的准确度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Feature Extraction with PCA and LTP Methods and Investigating the Effect of Dimensionality Reduction in the Bat Algorithm for Face Recognition
Face recognition is one of the challenging subjects of image processing. Facial recognition is often a biometric method that basically uses faces to recognize people. The face recognition system consists of three main steps: finding the face in the image, feature extraction and classification. The face recognition system faces challenges such as changes in lighting, changes in age, changes in facial expressions, etc. One of the important issues in this system is the algorithm execution speed. For this purpose, the dimensions of the feature vectors should be small enough, especially when the database is large. Since the face recognition system must be performed on a wide range of databases, dimensionality reduction techniques are required to reduce time and increase accuracy. Dimension reduction methods are used for this purpose. Two methods of dimensionality reduction, including LTP and PCA, are given in this research. In this research, first, the LTP feature vectors are extracted from the face image, and then the effective features are selected using the Bat algorithm. Therefore, this algorithm has three main phases of feature extraction, feature selection and classification. This algorithm is implemented on the ORL database, which contains 400 images of 40 different people with a size of 112×92 pixels. In addition to reducing the time required for testing, the proposed method has provided a very good accuracy of 99%.
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