This document describes a gait-based authentication system project. The project aims to authenticate individuals based on their unique walking gait using wearable sensors. It discusses implementing gait authentication using machine vision, floor sensors, or wearable sensors. The implementation phases include data gathering, feature extraction, modeling, training, and testing classifiers like neural networks and random forests to identify users based on their gait data. A web portal was created for data collection and evaluation of the gait authentication system.

1. Gait Based Authentication
System
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Project Guide
Mr. Vikas Mittal,
(HOD CS & E)
Project Members
Vatsal Agarwal
Vivek Kumar
Vijay Bhasin
Yash Agarwal

2. About Project
It aims to authenticate subject on the basis of one’s gait - i.e. method of
walking.
Everyone has it’s own unique gait - an important biometric.
Clinical research has shown that gait, the way a person walks or runs, is
sufficient to distinguish between individuals [1].
Can be thought of as unintrusive authentication method as one can easily get
authenticated without using tedious challenges as username - password /
PIN combination or fingerprint biometric. 2

3. About contd ..
Authentication is a specialized case of identification.
Aim to set a new standard in mobile security.
Project Site - bit.ly/gaitAuth
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4. Scope
● High end security solution
● Effective as uses minimal / no user interaction
● Can be used in security intensive arenas as :
○ High security compounds
○ Logging entry / exit
○ Access protected areas
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5. Implementation
Types of implementation possible
a. Machine Vision Technology (MVT) [3,4]
b. Floor Sensor Technology (FST) [5]
c. Wearable Sensor Technology (WST) [6]
WST is recognised as most approachable and newest of all.
Sensors in WST are attached to human body in various positions as pockets,
waist or shoes to record physical motions. 5

6. Implementation contd ..
Phases of Implementation
Gathering Data
Feature Extraction and Data Transformation
Modelling
Training
Testing
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7. 7

8. Gathering Data
3 axes accelerometer
data is logged into
csv format by an
android application [2]
Dedicated portal is
designed to
encourage
participation and data
upload facility is
provided [7] .
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9. Feature Extraction and Data Transformation
Gait features are extracted to compare between genuine and imposter
attempt.
Transformation process partitions the time series data into a fixed time non
overlapping windows and generates relatively simple features based on
data in window [8] .
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10. Feature Extraction contd ..
Features to be mined :
Average - Average sensor value each axis
Standard Deviation - SD for each axis
Average Absolute Difference - AAD between set of values and mean of these values
Time Between Peaks - Between sinusoidal waves
Binned Distribution - Range of values determined, x equal sized bins formed
Average Resultant Acceleration - For each of the sensor values in window, take square root 10

11. Modelling
Classifier Induction Algorithms are used.
The WEKA data mining suite is freely available and implements a large
number of classifier algorithms.
The algorithms that can prove effective for gait are :
Multilayer Perceptron (MLP) - Neural Network Based
Random Forest
Rotation Forest 11

12. Training
The task is to identify a user from pool of users based on sample of their
sample data.
Requires sample of data from all users in the population to use in training.
The transformed datasets associated with accelerometer data are used to
train and evaluate biometric identification models.
10 fold cross validation is used.
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13. Testing
Testing is done on the left datasets using the classifier algorithms.
Random users are picked and any x minute of data is chosen and classifier
model runs on the data to identify a genuine or imposter attempt.
The process is repeated to get accurate results.
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14. Validating
In authentication each user has their own model and when a sample of
sensor data is provided, the task is to determine if the sample belongs to
user or to an imposter.
The results are based on identifying each user based on a single x minutes
data sample(instance).
Overall accuracy will be the total number of correct predictions divided by
total number of predictions.
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15. GBAS Portal
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16. Upload Facility
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17. GBAS Portal Hits
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18. References
1. M. Nixon, T. Tan, and R. Chellapppa, Human Identification Based on Gait.
New York: Springer Science + Business Media Inc., 2006, ch. 1.
2. Accelerometer Acceleration Log
https://play.google.com/store/apps/details?id=com.alfav.applications.acc
elerometerlog
3. L. Lee, “Gait Analysis for classification”, Massachusetts Institute of
Technology-artificial Intelligence Laboratory, 2003.
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19. References contd ..
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4. L. Wang, T. Tan, W. Hu and H. Ning, “Automatic gait recognition based on
statistical shape analysis”, in IEEE Transactions on Image Processing, 2003.
5. J. Suutala, K. Fujinami and J. Roning, “Gaussian process person identifier based
on simple floor sensor”, in Proceedings of the 3rd European Conference on Smart
Sensing and Context, 2008.
6. A. Annadhorai, E. Guenterberg, J. Barnes and K. Haraga, “Human identification by
gait analysis”, in Proceedings of the 2nd International Workshop on Systems and
Networking Support for Health Care and Assisted Living Environments, 2008.
7. GBAS portal - bit.ly/gaitAuth, upload facility - bit.ly/gaitData
8. R windows - https://stat.ethz.ch/R-manual/R-
devel/library/stats/html/window.html

20. Contributors
1. Arpit Agarwal, Greater Noida
2. Shubhanshu Arora, Moradabad
3. Rajat Agarwal, Bangalore
4. Sugam, Moradabad
5. Vatsal, Vivek , Vijay, Yash - Moradabad
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