不可分小波域的BPCA人脸识别的开题报告

精品文档---下载后可任意编辑不可分小波域的 BPCA 人脸识别的开题报告摘要：人脸识别是一种应用广泛的生物识别技术，它可以辨别或验证人的身份。其中，主成分分析（PCA）是一种常用的人脸识别算法，具有较高的准确性和稳定性。但是，PCA 的计算复杂度很高，难以处理大规模的人脸数据。为了解决这个问题，本文提出了一种新的基于不可分小波域的 BPCA 人脸识别算法，该算法可以快速处理大规模的人脸数据，并且具有较高的准确性。本文首先介绍了人脸识别的背景及相关讨论，对 PCA 的原理和应用进行了深化的探讨。在此基础上，提出了一种基于不可分小波域的 BPCA人脸识别算法，该算法采纳不可分小波变换对人脸图像进行预处理，将人脸图像分解为多个子频带。然后，对每个子频带进行 BPCA 降维和分类处理，最终得到人脸识别的结果。实验结果表明，与传统的 PCA 人脸识别算法相比，基于不可分小波域的 BPCA 人脸识别算法具有更高的准确性和更快的计算速度。关键词：人脸识别；主成分分析；不可分小波变换；BPCA 算法Abstract：Face recognition is a widely used biometric technology that can identify or verify the identity of a person. Principal component analysis (PCA) is a commonly used face recognition algorithm with high accuracy and stability. However, the calculation complexity of PCA is high and it is difficult to process large-scale face data. In order to solve this problem, this paper proposes a new BPCA face recognition algorithm based on undecimated wavelet domain, which can quickly process large-scale face data and has high accuracy.The background and related research of face recognition are first introduced in this paper, and the principle and application of PCA are discussed in depth. Based on this, a BPCA face recognition algorithm based on undecimated wavelet domain is proposed, which uses undecimated wavelet transform to preprocess face images, decomposes face images into multiple subbands. Then, BPCA dimensionality reduction and classification processing are performed on each subband to obtain the face recognition result. The experimental results show that compared with the traditional PCA face recognition algorithm, the BPCA face recognition algorithm based on 精品文档---下载后可任意编辑undecimated wavelet domain has higher accuracy and faster calculation speed.Keywords: Face recognition; Principal component analysis; Undecimated wavelet transform; BPCA algorithm.