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BIOMETRIC GAIT RECOGNITION CMPE 58Z INTRODUCTION TO BIOMETRICS TERM PROJECT

BIOMETRIC GAIT RECOGNITION CMPE 58Z INTRODUCTION TO BIOMETRICS TERM PROJECT. MUSTAFA OZAN ÖZEN PINAR SAĞLAM LEVENT ÜNVER MEHMET YILMAZ. MOTIVATION. Gait : Particular way or manner of moving on foot . Gait Recognition : Identifying people with respect to their gait features .

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BIOMETRIC GAIT RECOGNITION CMPE 58Z INTRODUCTION TO BIOMETRICS TERM PROJECT

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  1. BIOMETRIC GAIT RECOGNITIONCMPE 58Z INTRODUCTION TO BIOMETRICS TERMPROJECT MUSTAFA OZAN ÖZENPINAR SAĞLAM LEVENT ÜNVER MEHMET YILMAZ

  2. MOTIVATION Gait: Particularwayormanner of moving on foot. GaitRecognition: Identifyingpeoplewithrespecttotheirgaitfeatures. Advantages: Can be used at distance Can be used at lowresolution Acceptablebypeople

  3. OUTLINE • General GaitRecognitionApproaches • CASIA Database • Theapproacheswecurrentlyused: • “AveragedSillhouettes” Approach. • “AbsoluteJointPositions” Approach. • “Abdelkader’sEigengait” Approach. • “Whatif it happens?” Approach.

  4. General GaitRecognitionApproaches Gait Recognition Approaches MV-Based FS-Based WS-Based Silhouette-Based Model-Based

  5. CASIA GaitDatabase • Inthisproject, CASIA GaitDataBaseA is used • CASIA GaitDataBase: • Has 20 differentpersons data. Eachperson has 12 differentsillhouettegait data set. But weonlyused 2 or 4 dataset (fromrighttoleftgait data). • Inotherwords, therewereone test andonetraining data set foreachperson. Each data set consists of max. 75, min. 37 frames

  6. CASIA GaitDataBase – SampleSillhouettes

  7. “AveragedSilhouettes” Appraoch • Silhouette Extraction • Gait Cycle Calculation • Averaged Silhouette Respresentation • Similarity Computation • Results and Discusion

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

  9. GaitCycleCalculation

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

  11. GaitCycleCalculation

  12. AveragedSilhouetteRepresentation

  13. AveragedSilhouetteRepresentation (DirectionCorrection)

  14. AveragedSilhouetteRepresentation (HeightNormalization)

  15. SimilarityComputation • Calculate Euclidean Distance • Form the Similarity Matrix

  16. ResultsAndDiscussion • EER = 58.9% • Closed Set Identification Rate = 73.68% • IndividualSilhouetteFrames = ~73% • AveragedSilhouette (Frompaper) = ~79% • Low EER => Lowqualitysilhouettes • Not sobadClosed Set Identification Rate

  17. AbsoluteJointPositions • 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. AbsoluteJointPositions • 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. Extracting Absolute Joint Positions • To extract absolute joint positions, the corresponding positions are clicked in each frame.

  20. Extracting Absolute Joint Positions

  21. Layout of jointpositionfeaturematrix & featurevector • Feature Matrix • FeatureVector

  22. 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.

  23. SpatioTemporalCorrelation • The next step is to perform the recognition by pattern classification. • Algorithm: • Each element of the class cluster one is compared withthe other class, and the distance is calculated. • Thetotal distance between all corresponding class elements are summed and a measure of thedistance of the two classes is calculated. • The training class which has the smallest distancefrom the query cluster is chosen to be the class (i.e. person) which the query belongs to.

  24. SpatioTemporalCorrelation

  25. Discussion • Thisprojectrecognise 7 person of 20 people. • Restrictions: • Thedatasetthatwehaveworked on is not qualified.

  26. Discussion • Restrictions: 2. Wedon’thaveenough data fortrainingand test set. 3. Anyotheradvancedclassificationmethods can be appliedratherthanspatiotemporalcorrelation

  27. Abdelkader’sEigengaitApproach • Abdelkader’s eigengait approach of gait recognition is also a silhouette – based technique. • Thistechniquecreates self similaritymatricesfromtheimagesequences. • Aftercreating self similaritymatrices, therows of thesematricesareappendedto form a singlefeaturevector. • Allthefeaturevectorsaregatheredtogetherand PCA is appliedtoprojectthe data into a newfeaturespacewhich is calledEigengait. • Finally k-NN is appliedtotheEigengait data forclassification.

  28. Abdelkader’sEigengaitApproach • Self SimilarityMatricesarecreatedbycomparingthesimilarity of pixelintensitiesover N frames. • Ot1 and Ot2 areextractedimages at time t1 and t2 respectively. • x and y valuesarerepresentingthepixels of theimage. • Bt1 is the minimum boundingboxsurroundingtheextractedobject.

  29. Abdelkader’sEigengaitApproach Self Similarity Plot

  30. Abdelkader’sEigengaitApproach Self Similarity Matrice Characteristics

  31. Abdelkader’sEigengaitApproach • 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. Discussion • SOTON Database will be used for the next experiments. (normalized, not noisy about 10 instances for each class)

  33. 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.

  34. Ozan’s “What if it happens?” approach • 2 ideas coming together • Using skeletons • Using Motion history images

  35. IF A picture is worth a thousand words ...

  36. What about a video?

  37. A little bit of results? Pure Skeleton Skeleton + time Pure Full Image Full image + time

  38. Whydidn’t it happen? 

  39. Comparison

  40. REFERENCES: • “AverageSillhouettes” Approach: • “Simplest Representation Yet for Gait Recognition: Averaged Silhouette” ZongyiLiuandSudeep Sarkar • “AbsoluteJointPositions” Approach: • “GaitRecognitionusingAbsoluteJointPositions” Mark RuaneDawson • “Abdelkader’sEigengait” Approach • “Motion-Based Recognition of People in EigenGait Space” Chiraz Ben Abdelkader

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