Activity-Biometrics: Person Identification from Daily Activities

Abstract

The University of Central Florida invention introduces a novel approach to person identification based on daily activities, addressing the limitations of traditional methods such as face recognition and gait analysis. Face recognition techniques, while advanced, often fail in real-world scenarios where facial features are not visible due to factors like long distances, environmental disturbances, occlusions (e.g., mask-wearing), and uncooperative subjects. Gait recognition, which analyzes walking patterns, also has limitations as individuals are not always walking in real-world situations. 

This invention focuses on identifying individuals based on a wide range of daily activities, such as sitting, taking off a jacket, drinking water, and more. These activities provide unique behavioral cues that can be instrumental in identifying individuals even when facial information is unavailable. The approach leverages video analysis to process both biometric features (e.g., body shape, gait) and non-biometric features (e.g., clothing, background) from video data.

Technical Details:   The UCF invention utilizes advanced video analysis techniques to process both biometric and non-biometric features from video data. Initially, an RGB video input containing a mix of biometric features (such as body shape and gait) and non-biometric features (such as clothing and background) is received. This video is passed through a sophisticated video encoder that extracts spatial-temporal features, which are divided into two streams. The "activity head" analyzes the specific activities depicted in the video, while the "actor head" distinguishes between the biometric and non-biometric features of the individual.

To enhance identification accuracy, the invention employs a bias-less distillation process, where a silhouette version of the video is processed through a teacher network to distill unbiased biometric features back into the main model, filtering out appearance-related biases. Additionally, a bias-learning technique distorts the original video to obscure biometric features while keeping non-biometric features intact, allowing the system to learn and compensate for appearance biases during training. The method concludes with a joint training process that refines both activity and actor features, enabling the system to use these enhanced features for accurate identification by comparing them against a pre-existing gallery of identities.

Benefit

Market Application