Person Identification from Contextual Motion

2026-06-11

AI Summary

This study addresses the limited effectiveness of existing methods in identity recognition based on individual motion styles, particularly in surveillance and authentication scenarios. The work proposes a novel interactive identity recognition paradigm that infers identity through sequences of visual prompts from the system and corresponding motion responses from the subject. Grounded in human information processing mechanisms, the approach employs a probabilistic generative model combined with Bayesian posterior updating and mutual information optimization to dynamically select the most discriminative visual stimuli, thereby maximizing mutual information between prompts and identity. Evaluated across five public datasets and a newly collected dataset comprising 4,476 records, the method achieves consistently high accuracy, significantly enhancing both recognition efficiency and robustness.

Abstract

We consider the problem of identifying people based on their motion styles. We present a generative model describing the action instance creation process and derive a probabilistic identity inference scheme for two common person identification scenarios motivated by the surveillance and authentication applications. We introduce a novel, \emph{interactive}, scenario for person identification from motion patterns. To this end, we formalize the identification process in the context of a sequential message exchange session between the subject and the system. The subject's behavior is modeled using a probabilistic generative model inspired by the Human Information Processing (HIP) paradigm. At each stage, the system presents a visual stimulus (a cue) to the subject and records their motion response. The cue is selected so as to maximize the mutual information of the expected response and the subject's identity. Once recorded, the response is used to update the a posteriori probability over possible subjects' identities. The process terminates once a sufficient classification confidence level is reached. To the best of our knowledge, this is the first time person identification is addressed in such interactive setting. We report high recognition rates on five publicly available datasets and our own novel dataset consisting of 4,476 recordings of 22 test subjects responding to 15 cues.

Problem

• Person Identification
• Motion Patterns
• Interactive Identification
• Behavioral Biometrics
• Contextual Motion

Innovation

• interactive person identification
• motion-based biometrics
• probabilistic generative model
• sequential cue-response
• mutual information optimization