基于离散余弦变换和稀疏表示的人脸识别

Face recognition based on DCT and sparse representation

传统的基于稀疏表示的人脸识别方法是基于人脸的整体特征的,这类方法要求每位测试者的人脸图像要有足够多幅,而且特征维度高,计算复杂,针对这一问题,提出一种基于离散余弦变换和稀疏表示的人脸识别方法,对人脸图像进行分块采样,对采样样本使用离散余弦变换和稀疏分解,然后使用一种类似于词袋的方法得到整幅图像的特征向量,最后使用相似度比较的方法进行分类识别。实验表明,在此提出的方法比传统的基于稀疏表示的人脸识别方法在训练样本较少时效果更好。

Traditional face recognition methods based on sparse representation are based on holistic feature of face image. The methods requires enough face images for each test person and the high dimensional feature,and has computational complexi？ty. Aiming at these shortcomings,a face recognition method based on discrete cosine transform（DCT）and sparse representation is proposed,which divides an image into regions,samples in each region,decomposes the samples by DCT and sparse represen？tation,gets feature vector of the whole image with a method like bag？of？word,and then classifies and identifies them by similari？ty comparing method. The experiment results indicate that the method outperform the traditional face recognition methods based on sparse representation when there are few training samples.