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BIOMETRIC GAIT RECOGNITIONCMPE 58Z INTRODUCTION TO BIOMETRICS
TERM PROJECT
MUSTAFA OZAN ÖZENPINAR SAĞLAMLEVENT ÜNVER
MEHMET YILMAZ
MOTIVATION 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 distance2. Can be used at low resolution3. 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.
OUTLINE
General Gait Recognition Approaches
Gait Recognition Approaches
MV-Based FS-Based WS-Based
Silhouette-Based
Model-Based
CASIA Gait Database In this project, CASIA GaitDataBaseA is
used CASIA GaitDataBase:i. 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).
ii. 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
CASIA Gait DataBase – Sample Sillhouettes
“Averaged Silhouettes” Appraoch
Silhouette Extraction Gait Cycle Calculation Averaged Silhouette Respresentation Similarity Computation Results and Discusion
Silhouette Extraction GMM to extract silhouettes
Unable to download the database
Sample silhouettes from CASIA Database
Gait Cycle Calculation
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Gait Cycle Calculation Problem in Gait Cycle Calculation
Can not estimate gait cycle
What to do?????
Gait Cycle Calculation
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Averaged Silhouette Representation
Averaged Silhouette Representation (Direction
Correction)
Averaged Silhouette Representation (Height
Normalization)
Similarity Computation Calculate Euclidean Distance
Form the Similarity Matrix
Results And Discussion
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 Rate0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
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Absolute Joint Positions 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 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.
Extracting Absolute Joint Positions To extract absolute joint positions, the
corresponding positions are clicked in each frame.
Extracting Absolute Joint Positions
Layout of joint position feature matrix & feature vector
Feature Matrix
Feature Vector
Principle Component Analysis 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.
Spatio Temporal Correlation
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.
Spatio Temporal Correlation
Discussion This project recognise 7 person of 20
people. Restrictions: 1.The dataset that we have worked on is not
qualified.
Discussion 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
Abdelkader’s Eigengait Approach
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.
Abdelkader’s Eigengait Approach
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.
Abdelkader’s Eigengait Approach
Self Similarity Plot
Abdelkader’s Eigengait Approach
Self Similarity Matrice Characteristics
Abdelkader’s Eigengait Approach
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.
Discussion
SOTON Database will be used for the next experiments.(normalized, not noisyabout 10 instances for each class)
Discussion
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.
Ozan’s “What if it happens?” approach 2 ideas coming together
◦ Using skeletons◦ Using Motion history images
A picture is worth a thousand words
IF...
What about a video?
A little bit of results?
Pure Skeleton Skeleton + time
Pure Full Image Full image + time
Why didn’t it happen?
ComparisonAveraged Silhouette(Paper)
Averaged Silhouette(Impl.)
Absolute Joint Positions(Paper)
Absolute Joint Positions(Impl.)
Eigengait Approach(Paper)
Eigengait Approach(Impl.)
Identification Rate
79% 73% 55% 35% 93% 25%
“Average Sillhouettes” Approach:1. “Simplest Representation Yet for Gait Recognition:
Averaged Silhouette” Zongyi Liu and Sudeep Sarkar “Absolute Joint Positions” Approach:1. “Gait Recognition using Absolute Joint Positions” Mark
Ruane Dawson “Abdelkader’s Eigengait” Approach1. “Motion-Based Recognition of People in EigenGait Space”
Chiraz Ben Abdelkader
REFERENCES: