Learning gait component relationships by fusing logic and graphs using Markov Logic Networks

Ibrahim Venkat, Philippe De Wilde

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Gait recognition is a newly developing biometric which has potential to recognize people at a distance when application of other biometrics might not be feasible. We propose a new technique to represent and learn various gait component relationships using a recently developing statistical relational learning technique called Markov Logic Networks. Markov Logic Network is a robust statistical learning technique that fuses expressive first-order logic with probabilistic graphical models and prove to be efficient in handling noisy and uncertain data. Initially we derive component based pattern classifiers in the imaging domain using an automatic segmentation scheme and represent gait components and their relationships using first-order logic. Then we model and learn their characteristics using undirected graphs to finally classify gaits based on standard inference techniques. The proposed approach enables automatic gait recognition from low resolution videos and differs from conventional techniques which rely on manual markings on videos. We show that the proposed representation provide intuitive means to reason gait component relationships. Our results show that the proposed approach competes well with other state-of-the-art techniques.

Original languageEnglish
Title of host publication13th Conference on Information Fusion, Fusion 2010
Publication statusPublished - 2010
Event13th Conference on Information Fusion - Edinburgh, United Kingdom
Duration: 26 Jul 201029 Jul 2010


Conference13th Conference on Information Fusion
Abbreviated titleFusion 2010
Country/TerritoryUnited Kingdom


  • Biometrics
  • Gait recognition
  • Logic-based fusion
  • Markov Logic Networks


Dive into the research topics of 'Learning gait component relationships by fusing logic and graphs using Markov Logic Networks'. Together they form a unique fingerprint.

Cite this