RAPOO: An Efficient Privacy-Preserving Facial Expression Recognition via Mobile Crowdsensing

Bo Tian, Bowen Zhao, Yang Xiao, Yang Liu, Qingqi Pei, Yulong Shen

Abstract: Facial expression recognition is a technology that involves analyzing and interpreting human facial expressions to determine individual expressions or states. Mobile crowdsensing (MCS), a promising sensing paradigm, makes it easy to capture facial images and benefits facial expression recognition. Existing inference models for facial expression recognition usually rely on facial feature vectors or facial images, increasing privacy concerns about expression. For this reason, this paper proposes a privacy-preserving facial expression recognition scheme through MCS, named RAPOO, which falls in a client-server architecture. Roughly speaking, a user captures facial images using mobile devices and requests a recognition service provided by a cloud computing center. To protect the privacy of expressions, our approach focuses on designing secure computation protocols required by facial expression recognition necessarily, such as secure vector distance calculation and secure top-$k$ query. These protocols enable facial expression recognition over encrypted data directly. To speed up the recognition and store encrypted feature vectors, a $k$-D tree data structure is introduced. The security analysis confirms that RAPOO effectively preserves the confidentiality of personal expressions. Extensive experimental evaluations show that our solution obtains a three-order-of-magnitude speedup in terms of computational overhead compared with the state-of-the-art.

External IDs:dblp:journals/tmc/TianZXLPS25