基于正交稀疏约束非负张量分解的人脸识别算法

Face recognition algorithm based on orthogonal and sparse constrained nonnegative tensor factorization

非负张量分解作为一种特征提取方法,以不会破坏数据的内部结构特征和可解释性强等优势在图像处理和模式识别领域得到广泛的应用。但是,该方法在提取人脸子特征时会存在以下两个问题:一是分解得到的基图像之间存在不必要的相关性,导致冗余信息较多,极占内存;二是编码不够稀疏导致图像表达方式不够简洁。这些问题都会极大的影响人脸识别的准确率。为了进一步提高人脸识别准确率,提出基于正交稀疏约束非负张量分解的人脸识别算法。首先,在传统的非负张量分解中添加正交稀疏约束,降低基图像之间的相关性并获得稀疏编码。其次,利用原始人脸图像和分解得到的基图像计算人脸的低维特征表示。最后,利用余弦相似度衡量低维特征间的相似性,判断两张人脸图像是否表示同一个人。通过在AR数据库和ORL数据库中进行实验,发现提出的改进算法能取得较好的识别效果。

As a feature extraction method,nonnegative tensor factorization has been widely used in image processing and pattern recognition for its advantages of preserving the internal structural features of data and strong interpretability.However,there are two problems in this method:one is that there is unnecessary correlation between the decomposed base images,which leads to more redundant information and takes up a lot of memory;the other is that the coding is not sparse enough,which leads to the expression of the image is not concise enough.These problems will greatly affect the accuracy of face recognition.In order to improve the accuracy of face recognition,a face recognition algorithm based on orthogonal and sparse constrained nonnegative tensor factorization is proposed.Firstly,orthogonal and sparse constraints are added to the traditional nonnegative tensor factorization to reduce the correlation between the base images and obtain sparse coding.Secondly,the original face image and the decomposed base image are used to calculate the low dimensional feature representation of the face.Finally,cosine similarity is used to measure the similarity between low-dimensional features and judge whether two face images represent the same person.Through experiments in AR database and ORL database,it is found that the improved algorithm can achieve better recognition effect.