基于部分标记数据进行人脸图像特征提取

Face feature extraction method based on part of labeled data

针对无监督特征提取的识别率低与监督特征提取需要大量标记的问题,提出一种基于部分标记数据的半监督判别分析(SSDPA)特征提取法。本文方法能实现图像数据降维,避免线性判别分析(LDA)存在的小样本问题,达到提高识别率的目的。算法对图像进行离散余弦变换(DCT)变换;根据DCT图像的频率分布,利用部分标记数据计算SSDP;优先搜索SSDP高的DCT图像信息。将本文方法与其它方法进行组合,在不同人脸数据库上进行了实验。实验证明了本文方法的有效性,用较低的代价获得了优于传统方法的识别率。

To counter low recognition accuracy of unsupervised feature extraction methods and too many part of labels of supervised feature extraction methods, a semi-supervised feature extraction method based on few labeled samples is proposed, which can reduce data dimensionality and avoid small sample size problem to improve the recognition accuracy. The image dataset is performed by discrete cosine transform,the semi-supervised discriminant power based on frequency distribution is computed with la beled samples,and high semi-supervised discriminant power is seeked to extract representative features. The proposed method is combined with other feature extraction methods, and codnueted on different face databases. The results prove that the method is efficient and can obtain higher recognition accuracy than traditional methods with lower cost.