MUSTAFA OZAN ÖZEN PINAR SAĞLAM LEVENT ÜNVER MEHMET YILMAZ.

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

0 10 20 30 40 50 60 70 802200

2400

2600

2800

3000

3200

3400

3600

0 10 20 30 40 50 60 70 802000

2200

2400

2600

2800

3000

3200

3400

Gait Cycle Calculation Problem in Gait Cycle Calculation

Can not estimate gait cycle

What to do?????

Gait Cycle Calculation

0 10 20 30 40 50 60 70 8025

30

35

40

45

50

55

60

65

70

75

0 10 20 30 40 50 60 70 8025

30

35

40

45

50

55

60

65

70

75

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

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

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.


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


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:

MUSTAFA OZAN ÖZEN PINAR SAĞLAM LEVENT ÜNVER MEHMET YILMAZ.

Documents

Transcript of MUSTAFA OZAN ÖZEN PINAR SAĞLAM LEVENT ÜNVER MEHMET YILMAZ.