This paper proposes a novel approach for privacy-preserving facial recognition based on the new feature computation technique: Local Binary Pattern from Temporal Planes (LBP-TP) that extracts information from only the $XT$ or $YT$ planes of a video sequence; in contrast to previous work that depend significantly on spatial information within the video frames. To our knowledge, this is the first known facial recognition work that does not rely on the spatial plane, nor that requires processing a facial input. The removal of this spatial reliance therefore withholds the facial appearance information from public view, where only one-dimensional spatial information that varies across time are extracted for recognition. Privacy is thus assured, yet without impeding the facial recognition task which is vital for many security applications such as street surveillance and perimeter access control. Experimental results indicate that the proposed method achieves accuracy of 99.56%, 98.19% and 100% for the recent CASME II, CAS(ME)² and Honda/UCSD databases respectively. In addition, a 66% reduction in the number of bytes required for storage and recognition was also observed from these experiments. The outcomes of this research demonstrate that privacy in face recognition can be preserved, without compromising its security (i.e., recognition accuracy) and efficiency.

本文基于新的特征计算技术，提出了一种用于保护隐私的面部识别的新方法：时空平面的局部二值模式（LBP-TP），该模式仅从时空平面中提取信息。 $XŤ$ 要么 $ÿŤ$视频序列的平面；与之前的工作明显依赖于视频帧内的空间信息相反。据我们所知，这是第一个不依赖空间平面，也不需要处理面部输入的面部识别工作。因此，消除这种空间依赖性可从公众视野中阻止面部外观信息，其中仅提取随时间变化的一维空间信息以进行识别。因此，确保了隐私，但又不妨碍面部识别任务，这对于许多安全应用（例如街道监视和外围访问控制）至关重要。实验结果表明，该方法对最近的CASME II，CAS（ME）^2的准确度达到99.56％，98.19％和100％。和本田/ UCSD数据库。此外，从这些实验中还发现，存储和识别所需的字节数减少了66％。这项研究的结果表明，可以保留面部识别中的隐私，而不会损害其安全性（即，识别准确性）和效率。