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.
Bibliographical noteThis work was supported by funds from the Provincial Natural Science Foundation of the Science and Technology Bureau of Jiangsu Province, China (Grant No. BK20180088 ), the China Postdoctoral Science Foundation (Grant No. 2019M651916 ), the Scientific Research Foundation of Nanjing University of Posts and Telecommunications, China (Grant No. NY218066 ), the Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (Grant No. KYCX18_0919 ) and the Natural Science Foundation of China (Grant No. 61871445 ).
- Contrast feature
- Multilayer compressed sensing
- Salient structural feature
- Visual privacy-preserving level evaluation