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Gait Recognition

Team members

Sarah Wong I-Mae (ESD), Vincent Leonardo (ESD), Sarah Ramjoo (ESD), Ngui Jia Xuan Sheriann (ISTD), Dharmapuri Krishna Sathvik (ISTD)


Matthieu De Mari, Ying Xu

Writing Instructors:

Grace Kong

Teaching Assistant:

Esra Oymak

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What is gait recognition?

Biometric recognition

Walking patterns

Identify individuals

Gait recognition is a biometric application to identify individuals by their walking pattern. This walking pattern is unique to everyone. By capturing the gait sequence, our project aims to identify individuals in the real world from their gait.

Why gait recognition?

Gait is your walking style and is unique to you.


Compared to other popular forms of recognition using fingerprints and facial features, gait recognition is non-intrusive, it works from a distance, and is unaffected by changes in appearance (bags, clothes, masks).

Comparing with other biometrics

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Helping society

GaitSearch aids in providing evidence in investigations to solve crimes, and may enhance public safety and security.

With a higher chances of getting identified, GaitSearch aids in deterring individuals from committing crime, thus reducing crime rates.

By identifying individuals, GaitSearch can help find missing individuals, bringing relief to their families and loved ones.

How we achieved this

Silhouette extraction

Subject silhouettes are segmented using a Deep Learning model called SCNet.

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Individual gait signatures

Our ensemble model recognises an individual's unique gait signature. It is based on GaitSet (2018) and GaitPart (2020)

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Local and global feature extraction

The model consists of several CNN blocks that allow local & global feature extraction.

Ease-of-use and metrics


50% rank-1 accuracy for real world recognition, jumps up to over 80% when considering rank-3 to rank-5 accuracies.


Improvement over the state-of-the-art models.


80% of users found GaitSearch to be fun and easy to use.

Let us create a better world by design!


student Sarah Wong I-Mae Engineering Systems and Design
student Vincent Leonardo Engineering Systems and Design
student Sarah Ramjoo Engineering Systems and Design
student Ngui Jia Xuan Sheriann Information Systems Technology and Design
student Dharmapuri Krishna Sathvik Information Systems Technology and Design
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