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首页> 外文会议>2015 International Conference on Energy Systems and Applications >Fast face recognition based on Wavelet Transform on PCA
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Fast face recognition based on Wavelet Transform on PCA

机译：基于PCA小波变换的快速人脸识别

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Today the word is moving towards the globalization in area of biometrics as an individual identification method. The techniques which are established for an identifying the individual using face as a biometric has become more importance in field of biometrics. The face database extracted leads the many application like photography, security surveillance, database identification etc. This paper includes the study of facial feature extraction techniques that are Principal Component Analysis (PCA) and Discrete Wavelet Transforms (DWT), hear the comparison of two given algorithms have been made with concerned to the rate of feature extraction for face recognition using the Principal Component Analysis (PCA) and the PCA using Discrete Wavelet Transforms (DWT). The proposed algorithm uses the concept of DWT for the image compression and PCA for the feature extraction and identification method. The limitations of the only PCA algorithm are a poor recognition speed and complex mathematical calculating load. To eliminate these limitations we are applying the DWT with different decomposition levels, i.e from level 0 to level 3 to facial image by using Daubechies Transform and applying the PCA for feature extraction process. The Euclidean Distance Measures system is used to find the nearest matching features in the whole database. In this paper the the mentioned algorithms are compared with their feature extraction and recognition time, the second parameter is the percentage of recognition of a test image. The results shows that the PCA with DWT applied gives higher recognition rate up to 93% than only PCA, with very less access time.
机译：今天，这个词正朝着生物识别领域的全球化方向发展，作为一种个人识别方法。建立的用于使用面部作为生物特征识别个人的技术在生物特征领域中变得越来越重要。提取的人脸数据库引领了摄影，安全监控，数据库识别等许多应用。本文包括人脸特征提取技术的研究，即主成分分析（PCA）和离散小波变换（DWT），听取两者的比较已经针对使用主成分分析（PCA）和使用离散小波变换（DWT）的PCA进行面部识别的特征提取率提出了一些算法。该算法将DWT用于图像压缩，将PCA用于特征提取和识别。唯一的PCA算法的局限性在于识别速度较差以及数学计算量复杂。为了消除这些限制，我们使用Daubechies变换并将PCA应用于特征提取过程，将具有不同分解级别（即从0级到3级）的DWT应用到面部图像。欧几里德距离测量系统用于在整个数据库中查找最接近的匹配特征。本文将上述算法与特征提取和识别时间进行比较，第二个参数是测试图像的识别百分比。结果表明，应用DWT的PCA与仅PCA相比，具有高达93％的更高识别率，并且访问时间非常短。

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