What question did this study set out to answer?

Establish a standardized challenge framework, baseline algorithm, and dataset to evaluate and characterize the solvability of automated human identification through gait recognition.

January 4, 2005IEEE Transactions on Pattern Analysis and Machine Intelligence

The humanID gait challenge problem: data sets, performance, and analysis

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Authors

SSSudeep Sarkar PPP. Jonathon Phillips ZLZ. Liu

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Overview

Benchmark study demonstrates video-based gait identification drops significantly under varying physical conditions across 122 subjects, indicating major challenges for real-world biometrics.

Key Points

Establish a standardized challenge framework, baseline algorithm, and dataset to evaluate and characterize the solvability of automated human identification through gait recognition.
Evaluated a baseline recognition algorithm that performs background subtraction for silhouette estimation and temporal correlation matching across 12 benchmark experiments.
Tested performance across 1,870 video sequences from 122 subjects under variations in five covariates: viewing angle, shoe type, walking surface, carrying a briefcase, and elapsed time.
Identification rates ranged from 78 percent in the least demanding experiment to 3 percent in the most challenging scenario.
All five evaluated covariates exerted statistically significant negative effects on recognition accuracy, with walking surface type and elapsed time causing the most substantial performance drops.

Cite This Study

Sarkar et al. (2005) studied this question.

synapsesocial.com/papers/6a152a6ed73ae7522a4e2134 https://doi.org/10.1109/tpami.2005.39

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January 4, 2005IEEE Transactions on Pattern Analysis and Machine Intelligence

The humanID gait challenge problem: data sets, performance, and analysis

View Full Paper

Ask AI

Bookmark

Authors

SSSudeep Sarkar PPP. Jonathon Phillips ZLZ. Liu

Discussion

Member takes

Overview

Benchmark study demonstrates video-based gait identification drops significantly under varying physical conditions across 122 subjects, indicating major challenges for real-world biometrics.

Key Points

Establish a standardized challenge framework, baseline algorithm, and dataset to evaluate and characterize the solvability of automated human identification through gait recognition.
Evaluated a baseline recognition algorithm that performs background subtraction for silhouette estimation and temporal correlation matching across 12 benchmark experiments.
Tested performance across 1,870 video sequences from 122 subjects under variations in five covariates: viewing angle, shoe type, walking surface, carrying a briefcase, and elapsed time.
Identification rates ranged from 78 percent in the least demanding experiment to 3 percent in the most challenging scenario.
All five evaluated covariates exerted statistically significant negative effects on recognition accuracy, with walking surface type and elapsed time causing the most substantial performance drops.

Cite This Study

Sarkar et al. (2005) studied this question.

synapsesocial.com/papers/6a152a6ed73ae7522a4e2134 https://doi.org/10.1109/tpami.2005.39

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