MUSTAFA OZAN ÖZEN
PINAR SAĞLAM
LEVENT ÜNVER
MEHMET YILMAZ
Gait: Particular way or manner of moving on
foot.
 Gait Recognition: Identifying people with
respect to their gait features.
 Advantages:
1. Can be used at distance
2. Can be used at low resolution
3. Acceptable by people

General Gait Recognition Approaches
 CASIA Database
 The approaches we currently used:
1. “Averaged Sillhouettes” Approach.
2. “Absolute Joint Positions” Approach.
3. “Abdelkader’s Eigengait” Approach.
4. “What if it happens?” Approach.

Gait Recognition
Approaches
MV-Based
FS-Based
SilhouetteBased
Model-Based
WS-Based
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ii.
In this project, CASIA GaitDataBaseA is used
CASIA GaitDataBase:
Has 20 different persons data. Each person
has 12 different sillhouette gait data set.
But we only used 2 or 4 dataset (from right
to left gait data).
In other words, there were one test and one
training data set for each person. Each data
set consists of max. 75, min. 37 frames
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Silhouette Extraction
Gait Cycle Calculation
Averaged Silhouette Respresentation
Similarity Computation
Results and Discusion

GMM to extract silhouettes
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Unable to download the database
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Sample silhouettes from CASIA Database
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Problem in Gait Cycle Calculation
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Can not estimate gait cycle
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What to do?????
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Calculate Euclidean Distance
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Form the Similarity Matrix
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EER = 58.9%
Closed Set Identification
Rate = 73.68%
Individual Silhouette Frames
= ~73%
Averaged Silhouette (From
paper) = ~79%
Low EER
=> Low quality silhouettes
Not so bad Closed Set
Identification Rate
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In the case of this project, the feature points
are the position of the joints.
PCA is applied to these feature points and the
feature size is reduced.
Then, spatio temporal correlation is used for
classifying.


Absolute joint
positions – the
physical positions of
each joint in each
frame can be used as
a basis for gait
signature.
8 absolute joint
positions of each
frame are used as
feature points.

To extract absolute joint positions, the
corresponding positions are clicked in each
frame.


Feature Matrix
Feature Vector


A person is identified by one feature vector.
After PCA, we projected feature vector into a
feature space which gives the best level of
recognition.
The next step is to perform the recognition by
pattern classification.
 Algorithm:
1. Each element of the class cluster one is compared
with the other class, and the distance is calculated.
2. The total distance between all corresponding class
elements are summed and a measure of the distance
of the two classes is calculated.
3. The training class which has the smallest distance
from the query cluster is chosen to be the class (i.e.
person) which the query belongs to.

This project recognise 7 person of 20 people.
 Restrictions:
1. The dataset that we have worked on is not
qualified.

Restrictions:
2. We don’t have enough data for training and
test set.
3. Any other advanced classification methods
can be applied rather than spatio temporal
correlation
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Abdelkader’s eigengait approach of gait recognition is
also a silhouette – based technique.
This technique creates self similarity matrices from the
image sequences.
After creating self similarity matrices, the rows of these
matrices are appended to form a single feature vector.
All the feature vectors are gathered together and PCA is
applied to project the data into a new feature space
which is called Eigengait.
Finally k-NN is applied to the Eigengait data for
classification.
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Self Similarity Matrices are created by comparing the
similarity of pixel intensities over N frames.
Ot1 and Ot2 are extracted images at time t1 and t2
respectively.
x and y values are representing the pixels of the image.
Bt1 is the minimum bounding box surrounding the
extracted object.
Self Similarity Plot
Self Similarity Matrice Characteristics

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Calculate the k – nearest neighbor to the
unclassified feature vector in the training set.
Determine the class which has the most points in
the k selected points.
SOTON Database will be used
for the next experiments.
(normalized, not noisy
about 10 instances for each class)
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Abdelkader’s Eigengait Approach has % 25
identification rate on CASIA Database.
The rate is very low because the dataset is not
sufficient for Eigengait approach.
We used 1-NN classifier because we can create
only one self similarity matrix for each class.
Data is not normalized according to the phases and
cycles which is very essential for sel similarity
matrices.

2 ideas coming together
◦ Using skeletons
◦ Using Motion history images
IF
...
Pure Skeleton
Pure Full Image
Skeleton + time
Full image + time
Averaged Averaged
Silhouette Silhouette
(Paper)
(Impl.)
Identification
Rate
79%
73%
Absolute
Joint
Positions
(Paper)
55%
Absolute Eigengait Eigengait
Joint
Approach Approach
Positions (Paper)
(Impl.)
(Impl.)
35%
93%
25%

1.
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1.
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1.
“Average Sillhouettes” Approach:
“Simplest Representation Yet for Gait Recognition: Averaged
Silhouette” Zongyi Liu and Sudeep Sarkar
“Absolute Joint Positions” Approach:
“Gait Recognition using Absolute Joint Positions” Mark
Ruane Dawson
“Abdelkader’s Eigengait” Approach
“Motion-Based Recognition of People in EigenGait Space”
Chiraz Ben Abdelkader