基于小波变换和改进PCA的人脸特征提取算法

针对在人脸图像高维数据降维时单纯使用主成分分析(PCA)算法的提取精度和速度受限问题,  提出一种基于小波变换和改进PCA的混合特征提取算法. 该方法首先对人脸图像进行小波分解, 选取低频分量对人脸图像进行特征提取；然后利用改进的PCA算法进行主成分提取, 获得代表人脸特征的特征向量; 最后将该算法应用于Olivetti Faces人脸库数据集的图像分类. 实验结果表明, 经过该混合算法处理后的图像特征数据, 由卷积神经网络(CNN)算法分类识别时准确率提升10%, 识别速度提高约37%.

Face Feature Extraction Algorithim Based on Wavelet Transform and Improved Principal Component Analysis

Aiming at the problem that extraction accuracy and speed were limited when using principal component analysis (PCA) algorithm only in face image high-dimensional data dimensionality reduction, we proposed a hybrid feature extraction algorithm based on wavelet transform and improved PCA. Firstly, the face image was decomposed by wavelet, and low-frequency component was selected for feature extraction. Secondly, the improved PCA algorithm was used for principal component extraction to obtain the feature vectors representing face features. Finally, the algorithm was applied to the image classification of Olivetti Faces  dataset. The experimental results show that the recognition accuracy is improved by 10% and the recognition speed is improved by about 37% when the image feature data processed by the hybrid algorithm are classified and recognized by convolutional neural network (CNN) algorithm.