Recognition and classification tasks in images or videos are ubiquitous, but they can lead to privacy issues. People increasingly hope that camera systems can record and recognize important events and objects, such as real-time recording of traffic conditions and accident scenes, elderly fall detection, and in-home monitoring. However, people also want to ensure these activities do not violate the privacy of users or others. The sparse representation classification and recognition algorithms based on compressed sensing (CS) are robust at recognizing human faces from frontal views with varying expressions and illuminations, as well as occlusions and disguises. This is a potential way to perform recognition tasks while preserving visual privacy. In this paper, an improved Gaussian random measurement matrix is adopted in the proposed multilayer CS (MCS) model to realize multiple image CS and achieve a balance between visual privacy-preserving and recognition tasks. The visual privacy-preserving level evaluation for MCS images has important guiding significance for image processing and recognition. Therefore, we propose an image visual privacy-preserving level evaluation method for the MCS model (MCS-VPLE) based on contrast and salient structural features. The basic concept is to use the contrast measurement model based on the statistical mean of the asymmetric alpha-trimmed filter and the salient generalized center-symmetric local binary pattern operator to extract contrast and salient structural features, respectively. The features are fed into a support vector regression to obtain the image quality score, and the fuzzy c-means algorithm is used for clustering to obtain the final evaluated image visual privacy-preserving score. Experiments on three constructed databases show that the proposed method has better prediction effectiveness and performance than conventional methods.

图像或视频中的识别和分类任务无处不在，但它们可能导致隐私问题。人们越来越希望摄像机系统可以记录和识别重要的事件和对象，例如交通状况和事故现场的实时记录，老人跌倒检测以及家庭监控。但是，人们还希望确保这些活动不会侵犯用户或其他人的隐私。基于压缩感知（CS）的稀疏表示分类和识别算法在从正面查看具有变化的表情和照明以及遮挡和伪装的人脸时具有强大的鲁棒性。这是在保留视觉隐私的同时执行识别任务的潜在方法。在本文中，在提出的多层CS模型中，采用了改进的高斯随机测量矩阵来实现多图像CS，并在视觉隐私保护和识别任务之间取得了平衡。MCS图像的视觉隐私保护级别评估对于图像处理和识别具有重要的指导意义。因此，我们提出了一种基于对比度和显着结构特征的MCS模型（MCS-VPLE）图像视觉隐私保护级别评估方法。基本概念是使用基于非对称alpha滤镜的统计平均值和显着的广义中心对称局部二进制模式算子的对比度测量模型分别提取对比度和显着的结构特征。将特征输入支持向量回归中以获得图像质量得分，然后使用模糊c均值算法进行聚类以获得最终评估的图像视觉隐私保护得分。在三个构造的数据库上进行的实验表明，该方法具有比常规方法更好的预测效果和性能。