1. MUSTAFA OZAN ÖZEN PINAR SAĞLAM LEVENT ÜNVER MEHMET YILMAZ

2. 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 distance 2. Can be used at low resolution 3. Acceptable by people

3.  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.

4. General Gait Recognition Approaches Gait Recognition Approaches MV-BasedFS-BasedWS-Based Silhouette- Based Model-Based

5.  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

7.  Silhouette Extraction  Gait Cycle Calculation  Averaged Silhouette Respresentation  Similarity Computation  Results and Discusion

8.  GMM to extract silhouettes  Unable to download the database  Sample silhouettes from CASIA Database

10.  Problem in Gait Cycle Calculation  Can not estimate gait cycle  What to do?????

15.  Calculate Euclidean Distance  Form the Similarity Matrix

16.  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

17.  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.

18.  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.

19.  To extract absolute joint positions, the corresponding positions are clicked in each frame.

21.  Feature Matrix  Feature Vector

22.  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.

23.  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.

25.  This project recognise 7 person of 20 people.  Restrictions: 1. The dataset that we have worked on is not qualified.

26.  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

27.  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.

28.  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.

29. Self Similarity Plot

30. Self Similarity Matrice Characteristics

31.  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.

32.  SOTON Database will be used for the next experiments. (normalized, not noisy about 10 instances for each class)

33.  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.

34.  2 ideas coming together ◦ Using skeletons ◦ Using Motion history images

35. IF...

37. Pure SkeletonSkeleton + time Pure Full ImageFull image + time

39. Averaged 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%

40.  “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” Approach 1. “Motion-Based Recognition of People in EigenGait Space” Chiraz Ben Abdelkader