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- 1. Defense of a Doctoral DissertationComputer Science and EngineeringUniversity of South Florida<br />Facial Skin Motion Properties from Video:Modeling and Applications<br />Vasant Manohar<br />Examining CommitteeAutar K. Kaw, Ph.D. – ChairpersonDmitry B. Goldgof, Ph.D. – Co-Major ProfessorSudeep Sarkar, Ph.D. – Co-Major ProfessorRangachar Kasturi, Ph.D.Tapas K. Das, Ph.D.Thomas A. Sanocki, Ph.D.<br />October 29, 2009<br />
- 2. 2<br />Presentation Outline<br />Introduction<br />Motivation, existing work, overview of the developed method<br />Strain-based Characterization<br />Finite Difference Method (profile faces)<br />Finite Element Method (frontal faces)<br />Material Constants-based Characterization<br />Matching faces using Young’s modulus values<br />Conclusions<br />Contribution to research, literature, ideas for future work<br />10/29/2009<br />2<br />
- 3. 3<br />A Generic Example<br />Is it possible to find a discriminative feature between the two balls?<br />3<br />10/29/2009<br />
- 4. 4<br />The Face Analogy<br />Is it possible to extract a stable feature between the camouflage face and the normal one?<br />4<br />10/29/2009<br />
- 5. 5<br />Deformable Modeling of Soft Tissues<br />Applications<br />HCI: facial expression recognition –Essa and Pentland (1997)<br />Age estimation: Kwon and Lobo (1999)<br />Person Identification: Zhang et al. (2004), Pamudurthy et al. (2005), Manohar et al. (2007)<br />Classes of approach<br />Physical models<br />Non-physical models<br />Review and applications: Metaxas (1996), Gibson and Mirtich (1997)<br />5<br />10/29/2009<br />
- 6. 6<br />Physical Models<br />Issues:<br />Observed physical phenomena can be very complex<br />Solving underlying partial differential equations (PDEs) requires substantial computational cost<br />Solution strategy<br />Find an adequate simplified model of given problem covering essential observations<br />Apply efficient numerical techniques for solving the PDEs<br />Our proposal<br />Strain pattern extracted from non-rigid facial motion as a simplified and adequate way <br />Modeling Young’s modulus of facial regions from observed motion<br />6<br />10/29/2009<br />
- 7. 7<br />Existing Work: Face Modeling and Biomechanics<br />Highly accurate models: Terzopoulos and Waters (1993)<br />Anatomical details of face: bones, musculature, and skin tissues<br />Drawback: high computational cost<br />Task driven reduced models: Essa and Pentland (1997)<br />Finite element model to estimate visual muscle activations and to generate motion energy templates for expression analysis<br />Drawback: automatic identification of action units that estimate the muscle activations<br />Our approach<br />Quantify soft tissue properties through their elasticity<br />Effectively represent them by means of strain maps<br />Model sub-regions of the face using their stiffness values<br />7<br />10/29/2009<br />
- 8. 8<br />Existing Work:Person Identification<br />Chen et al. (2001) augmented appearance-based method with facial motion to overcome illumination problems<br />Zhang et al. (2004) used strain pattern from 2 face images of closed and open jaw positions to reveal underlying muscular characteristics for recognition<br />Pamudurthy et al. (2005) used motion image derived from feature displacements<br />Our contribution<br />Extension of Strain maps to videos where the computation is automated<br />Substantiated with comprehensive system design and extensive experimental results<br />Explore an expression invariant approach using material constants<br />8<br />10/29/2009<br />
- 9. 9<br />Unique Features of this Work<br />Strain pattern, instead of image intensity, used as a classification feature<br />Related to the biomechanical properties of facial tissues that are distinct for each individual<br />Less sensitive to illumination differences (between registered and query sequences) and face camouflage<br />Finite element modeling based method enforces regularization<br />Mitigates issues related to automatic motion estimation<br />Using material constants for matching presents a unique opportunity for an expression invariant face matching process<br />No special imaging equipment is needed to capture facial deformation<br />9<br />10/29/2009<br />
- 10. 10<br />Theoretical Background<br />Optical Flow: Reflects the changes in the image due to motion<br />Strain: A measure to quantify the deformation undergone:<br />Principal Component Analysis: Dimensionality reduction technique that identifies the salient and rich information hidden in raw data<br />10<br />10/29/2009<br />
- 11. 11<br />System Flow:Face Matching using Strain Pattern<br />Input Video Sequence of Expression<br />Geometric Normalizationand Masking<br />Correspondence betweentwo subsequent frames<br />Principal Component Analysis<br />Optic Flow<br />Training<br />Coordinate point extraction<br />Displacement vectors forframe-pairs across sequence<br />Euclidean Subspace<br />Link flow valuesfrom each frame-pair<br />Testing<br />Displacement vector forthe complete sequence<br />Distances in <br />Projected Subspace<br />Strain ComputationModule<br />Nearest neighbor classifier<br />Strain Map of a Subject(Strain to Intensity)<br />Intra- & Inter-Subject Variation,ROC Curves<br />11<br />10/29/2009<br />
- 12. 12<br />Strain Computation from Dense Motion Field:The Finite Difference Method (FDM)<br />A linear strain tensor capable of describing small deformations is defined as:<br />In 2D image coordinates, this becomes:<br />Computing spatial derivatives (Central Difference Method)<br />Computing strain magnitude from normal strains:<br />12<br />10/29/2009<br />
- 13. 13<br />Motion and Strain Images<br />Video conditionsNormal Lighting Low Lighting Shadow Lighting Camouflage Face<br />Motion and Strain ImagesVideo Frame 12 Video Frame 15 Horizontal Motion Vertical MotionInput to Next Step Strain Magnitude Image<br />13<br />10/29/2009<br />
- 14. 14<br />Analysis of Strain as a Feature<br />Discriminatory Criterion Subject 1 Subject 2 Subject 3 Subject 4 Subject 5<br />Stability CriterionNormal Light Low Light Shadow Light Camouflage Face<br />14<br />10/29/2009<br />
- 15. 15<br />Experimental Set-up<br />A total of 60 subjects<br />All videos (from Canon Optura 20) were profile views of the face with opening the mouth as the expressionExperiments for FDM-based Strain Computation<br />Results were obtained using the Principal Component Analysis algorithm with Mahalanobis distance for computing metric scores<br />15<br />10/29/2009<br />
- 16. 16<br />Within-subject and Between-subject Variation<br />Receiver-Operating Characteristic Curve<br />Test-1 (Normal vs. Shadow Lighting)<br />16<br />10/29/2009<br />
- 17. 17<br />Within-subject and Between-subject Variation<br />Test-2 (Regular vs. Camouflage Faces)<br />17<br />10/29/2009<br />
- 18. 18<br />FDM-Based Method: Summary<br />Presented strain pattern as a unique and stable feature<br />Less vulnerable to illumination variations and face camouflage that often plague image analysis tasks<br />FDM carried out on an image grid makes the computational strategy efficient<br />Drawbacks<br />Requires a dense motion field<br />Restricted as a modeling platform<br />Limitations with respect to the material type<br />Doesn’t scale well for objects with irregular geometry<br />Requires extensive computational resources for data storage and system solving<br />18<br />10/29/2009<br />
- 19. 19<br />Strain Computation from Sparse Motion Field:The Finite Element Method (FEM)<br />State-of-the-art technique in physics-based modeling<br />Used for finding approximate solutions of partial differential equations<br />Approach is based on eliminating the differential equation completely<br />Primary challenge is in creating a numerically stable equation that approximates the equation to be studied<br />Relevance to our work<br />Easy incorporation of material constants associated with facial tissues<br />Sparse motion field would suffice<br />We used the commercial software, ANSYS, for FEM implementation<br />19<br />10/29/2009<br />
- 20. 20<br />Finite Element Face Model<br />Discretization<br />Geometry:<br />Linear elastic approximation of soft tissue behavior (Koch et al. 1996)<br />Equation of motion (Newton’s second law)<br />Strain-displacement equation<br />Constitutive equations (Hooke’s law)<br />20<br />10/29/2009<br />
- 21. 21<br />Finite Element Face Model<br />Each homogeneous and isotropic face region characterized by<br />Compressibility – Poisson’s Ratio<br />Stiffness – Young’s Modulus<br />Poisson’s Ratio of 0.4 (Gladilin 2002)<br />Learning Young’s Modulus<br />Concept of relative stiffness<br />Forehead – reference material; nose – highly rigid; eyes – varying stiffness; relative stiffness same for left and right cheeks<br />Optimization function: <br />Used 1/4th of motion field to drive the model; remaining 3/4th for validation<br />Done once per subject on normal lighting videos<br />21<br />10/29/2009<br />
- 22. 22<br />Motion and Strain Images<br />Video Conditions:<br />Motion Vectors:<br />Strain Images:<br />22<br />10/29/2009<br />
- 23. 23<br />Experimental Set-up<br />A total of 20 subjects<br />All videos were frontal views of the face with opening the mouth as the expressionExperiments for FEM-based Strain Computation<br />Results were obtained using the Principal Component Analysis algorithm with Mahalanobis distance for computing metric scores<br />23<br />10/29/2009<br />
- 24. 24<br />Non-Camouflage Experiments<br />Within-subject and Between-subject Variation (Test-1)<br />Within-subject and Between-subject Variation (Test-2)<br />24<br />10/29/2009<br />
- 25. 25<br />Camouflage Experiments<br />Within-subject and Between-subject Variation (Test-3)<br />25<br />10/29/2009<br />
- 26. 26<br />FEM-Based Method: Summary<br />Presented a computational strategy that just needs 1/25th of the motion vectors<br />The FE model enforces regularization<br />Mitigates issues related to automatic motion estimation<br />The model includes the material constants associated with facial tissues<br />Presented a first method to learn the material constants at a coarse level sufficient for accurate strain computation<br />Drawbacks<br />Uses just one expression to estimate Young’s modulus<br />Generic face model<br />Primitive search technique<br />Coarse sub-divisions<br />26<br />10/29/2009<br />
- 27. 27<br />Modeling Young’s Modulus fromMultiple Facial Expressions<br />Attempt a more accurate estimation of Young’s modulus by using motion from multiple expressions<br />Scalable matching process<br />Refine search technique for better estimation of values<br />Finer sub-divisions in the face model<br />Subject-specific face model conforming to individual’s facial feature locations<br />27<br />10/29/2009<br />
- 28. 28<br />System Flow:Face Matching using Material Constants<br />Input Video Sequence of Expression<br />ElasticFace: FE Face Modelwith learned material constants<br />Correspondence betweentwo subsequent frames<br />Optic Flow<br />Repeat for every expressionin the training set<br />Euclidean Space of Young’s Modulusof Face Patches<br />Displacement vectors forframe-pairs across sequence<br />Link flow valuesfrom each frame-pair<br />Testing<br />Displacement vector forthe complete sequence<br />Distances inElasticFace Space<br />Young’s ModulusLearning Module<br />Score-levelFusion techniques<br />Young’s Modulus Distributionof a Subject’s face<br />Intra- & Inter-Subject Variation,ROC Curves<br />28<br />10/29/2009<br />
- 29. 29<br />Modeling Algorithm<br />Step -1: Concept of relative stiffness; Forehead – reference material; nose – highly rigid; eyes – varying stiffness;<br />Step -2: Optimization function: <br />Step -3: Use 1/4th of motion field to drive the model; remaining 3/4th for computing the fitness function value<br />Step -4: Run a search algorithm to explore this solution space and use the converged values<br />Repeat Steps 1-4 for every sequence<br />Match based on parameter values along appropriate dimensions<br />29<br />10/29/2009<br />
- 30. 30<br />Facial Feature Detection<br />Motivation<br />System automation<br />Reducing computational cost of optic flow by just looking at the region of interest<br />Building a individual-specific face model<br />Viola-Jones Object Detector<br />Rectangular Haar-like binary feature wavelets<br />Cascade of weak classifiers<br />We used the OpenCV implementation of the Haar Object detection<br />Feature detection results on the BU dataset<br />30<br />10/29/2009<br />
- 31. 31<br />Face Model<br />Finer sub-divisions to attemptdefining a richer FE model<br />Specific to every subject basedon the results from the featuredetection step<br />31<br />10/29/2009<br />
- 32. 32<br />Search Algorithm<br />Plot of the fitness function<br />Objective function is not smooth<br />Multiple local optima<br />32<br />10/29/2009<br />
- 33. 33<br />Gradient-based vs. Random Algorithms<br />Gradient based approaches<br />Often progress slowly when the number of parameters are large<br />Make no allowance for multiple optima<br />Random algorithms then seem to be a reasonable choice<br />Course of the algorithm is decided by random numbers<br />Genetic Algorithms are a particular class of evolutionary algorithms (EA)<br />33<br />10/29/2009<br />
- 34. 34<br />Genetic Coding<br />Young’s modulus of regionsas the chromosome in GA<br />One-to-one mapping<br />Each chromosome in the poolrepresents a possible Young’smodulus distribution<br />34<br />10/29/2009<br />
- 35. 35<br />GA Parameter Settings<br />From the findings in literature for a similar domain, we use the following settings for the GA<br />We used a Gaussian mutation operator with mean = 0 and standard deviation = 1<br />35<br />10/29/2009<br />
- 36. 36<br />Training<br />Out of the 6 expressions, use 5 to estimate the Young’s modulus values<br />At least 40% of the elements in a region should deform in order to be considered for optimization<br />A note on sad, fear, and angry expressions<br />Used the converged values of Young’s modulus for regions where there was substantial deformation<br />Use the mean of converged values from multiple expressions as the final value for the region<br />36<br />10/29/2009<br />
- 37. 37<br />Multi-Feature Classification Systems:Combination Rules<br />Treat the Young’s modulus from each patch as a separate feature<br />Numerous combination techniques: sensor-level, feature-level, score-level, and decision-level<br />Popular score-level fusion techniques<br />Product Rule<br />Sum Rule<br />Max Rule<br />We investigate both Sum and the Max Rule<br />37<br />10/29/2009<br />
- 38. 38<br />Binghamton University 4D Facial ExpressionDataset: BU-4DFE<br />High-resolution (1040 x 1329) 3D dynamic facial expression database<br />Objective – analyze facial behavior in dynamic 3D space<br />Video rate – 25 frames per second<br />Six prototypical expressions: anger, disgust, happiness, fear, sadness, and surprise<br />101 subjects (58 female and 43 male) with wide ethnic/racial variety<br />38<br />10/29/2009<br />
- 39. 39<br />Experiments:Non-rigid Motion Tracking<br />Given a subset of motion vectors, we can estimate displacements in other regions using the equation of motion<br />Evaluation<br />Compare against Black and Anandan optic flow output<br />Generate fairly identical dense motion field from a sparse set of motion vectors<br />Compare against a simple bi-cubic interpolation method<br />Emphasize the value added by modeling of material constants in the deformation domain<br />39<br />10/29/2009<br />
- 40. 40<br />Experiments:Non-rigid Motion Tracking<br />Snapshot of the table comparing the two methods<br />Observations:<br />Average error from the model is within 7% and the worst case error is within 11%<br />Average error from the model is always less than the interpolation technique<br />40<br />10/29/2009<br />
- 41. 41<br />Experiments:Expression Invariant Matching<br />A total 0f 40 subjects (20 male and 20 females)<br />Performed a leave-one-expression-out experiment where we train on 5 expressions and test on the 6th expression; Repeat tests by changing the test expression<br />Investigated both Sum and Max rule<br />Metric computation only along relevant regions<br />40% threshold as earlier<br />41<br />10/29/2009<br />
- 42. 42<br />Experiments:Expression Invariant Matching<br />42<br />10/29/2009<br />
- 43. 43<br />Experiments:Expression Invariant Matching<br />43<br />10/29/2009<br />
- 44. 44<br />Experiments:Expression Invariant Matching<br />A first step towards expression invariant matching of faces<br />Due to lack of deformation, performance for some query expressions are not good: sadness, fear, or anger<br />The disparity in performance aligns with findings in literature<br />Max-rule outperforms sum-rule in almost all the tests<br />44<br />10/29/2009<br />
- 45. 45<br />Modeling Young’s modulus:Summary<br />Presented a method for modeling material constants (Young’s modulus) in sub-regions of the face<br />Efficient way of describing underlying material properties<br />Deformable modeling techniques are gauged by their simplicity and adequacy<br />First and novel attempt for expression invariant matching of face templates<br />45<br />10/29/2009<br />
- 46. 46<br />Conclusions<br />Used strain pattern an effective and efficient way of characterizing the material properties of facial soft tissues <br />Impact on applications such as facial expression recognition, age estimation, and person identification from video<br />Discussed two methods for computing strain pattern<br />FDM-based: efficient when carried out on image grid;<br />FEM-based: better characterization of facial tissues by incorporating relative material properties; works well with sparse motion field<br />Experiments emphasize that strain pattern is a discriminative and stable feature<br />Value further justified by performance under shadow lighting and camouflage<br />46<br />10/29/2009<br />
- 47. 47<br />Conclusions<br />Developed a method for modeling material constants from the motion observed in multiple facial expressions<br />Impact on deformable modeling techniques<br />Presented a novel expression invariant matching strategy<br />Impact on biometrics<br />Due to limited population size, this study so far can only provide a baseline evaluation on performance of the presented methods<br />47<br />10/29/2009<br />
- 48. 48<br />Conclusions<br />Intellectual Merit<br />Facial strain pattern adds a new dimension in characterizing the face<br />Important auxiliary information that can be exploited in multimodal techniques<br />Fosters a newer way to capture facial dynamics from video<br />Presents a very first attempt on matching faces with different expressions<br />Presents a simple and adequate way of modeling deformable objects (implications on real-time methods)<br />Broader Impact<br />Addresses the long-standing problem of motion analysis of elastic objects<br />Cross-disciplinary nature<br />Applying image analysis algorithms for material property characterization of facial soft tissues and its applications<br />Utilizes video processing to enhance our abilities to make unique discoveries through facial dynamics in video<br />48<br />10/29/2009<br />
- 49. 49<br />Future Directions<br />Fusion with intensity information in a recognition framework<br />Further justify the orthogonal information provided by strain maps<br />Capture the dynamics inherent in a facial expression<br />Snapshots of the variation of strain pattern<br />Use manifolds of strain patterns in image analysis tasks<br />49<br />10/29/2009<br />
- 50. 50<br />Contribution to Literature<br />Facial Motion Analysis:<br />V. Manohar, Y. Zhang, D. Goldgof, and S. Sarkar, “Facial Strain Pattern as a Soft Forensic Evidence”, In the Eighth IEEE Workshop on Applications of Computer Vision, Page: 42, 2007<br />V. Manohar, Y. Zhang, D. Goldgof, and S. Sarkar, “Video-based Person Identification using Facial Strain Pattern”, To be submitted to the IEEE Transactions on System, Man, and Cybernetics – Part B<br />V. Manohar, M. Shreve, D. Goldgof, and S. Sarkar, “Finite Element Modeling of Facial Deformation in Videos for Computing Strain Pattern”, In the International Conference on Pattern Recognition, ISBN 978-1-4244-2174-9, Pages: 1-4<br />M. Shreve, S. Godavarthy, V. Manohar, D. Goldgof, and S. Sarkar, “Towards Macro- and Micro-Expression Spotting in Video using Strain Patterns”, In the IEEE Workshop on Applications of Computer Vision, 2009<br />Y. Zhang, J.R. Sullins, D. Goldgof, and V. Manohar, “Computing Strain Elastograms of Skin Using an Optical Flow Based Method”, In the Fifth International Conference on the Ultrasonic Measurement and Imaging of Tissue Elasticity, 2006<br />Medical Imaging:<br />Y. Qiu, V. Manohar, V. Korzhova, X. Sun, and D. Goldgof, “Two-View Mammography Registration using 3D Finite Element Model of the Breast”, Submitted to Computerized Medical Imaging and Graphics<br />Y. Qiu, X. Sun, V. Manohar, and D. Goldgof, "Towards Registration of Temporal Mammograms by Finite Element Simulation of MR Breast Volumes", In the SPIE Medical Imaging: Visualization, Image-guided Procedures, and Modeling, Vol. 6918, 6918-86, 2008<br />Y. Zhang, R.W. Kramer, D. Goldgof, and V. Manohar, "Development of a Robust Algorithm for Imaging Complex Tissue Elasticity", In the Fifth International Conference on the Ultrasonic Measurement and Imaging of Tissue Elasticity, 2006<br />50<br />10/29/2009<br />
- 51. 51<br />Contribution to Literature<br />Performance Evaluation:<br />R. Kasturi, D. Goldgof, P. Soundararajan, V. Manohar, J. Garofolo, R. Bowers, M. Boonstra, V. Korzhova, and J. Zhang, "Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 2, Pages: 319-336, Feb 2009<br />V. Manohar, P. Soundararajan, H. Raju, D. Goldgof, R. Kasturi, and J. Garofolo, "Performance Evaluation of Object Detection and Tracking in Video", In the Seventh Asian Conference on Computer Vision, LNCS 3852, pp: 151-161, 2006<br />V. Manohar, P. Soundararajan, M. Boonstra, H. Raju, D. Goldgof, R. Kasturi, and J. Garofolo, "Performance Evaluation of Text Detection and Tracking in Video", In the Seventh IAPR Workshop on Document Analysis Systems, LNCS 3872, pp: 576-587, 2006<br />V. Manohar, M. Boonstra, V. Korzhova, P. Soundararajan, D. Goldgof, R. Kasturi, S. Prasad, H. Raju, R. Bowers, and J. Garofolo, "PETS vs. VACE Evaluation Programs: A Comparative Study", In the Ninth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance(PETS), pp: 1-6, In Conjunction with CVPR, 2006<br />V. Manohar, P. Soundararajan, V. Korzhova, M. Boonstra, D. Goldgof, R. Kasturi, R. Bowers, and J. Garofolo, "A Baseline Algorithm for Face Detection and Tracking in Video", In the SPIE Europe Symposium on Security and Defence: Optics and Photonics for Counter-Terrorism and Crime-Fighting, Vol. 6741, 6741-09, 2007<br />51<br />10/29/2009<br />
- 52. 52<br />QUESTIONS?<br />
- 53. 53<br />Motion Estimation: Optical Flow Method<br /><ul><li>Reflects the changes in the image due to motion
- 54. Computation is based on the following assumptions:
- 55. observed brightness of any object point is constant over time
- 56. nearby points in the image plane move in a similar manner
- 57. Minimization problem:(brightness const.) (smoothness const.)
- 58. Robust estimation framework (Black and Anandan, 1996)
- 59. Recast the least squared formulations with a different error-norm function instead of quadratic
- 60. Coarse-to-fine strategy
- 61. Construct a pyramid of spatially filtered and sub-sampled images
- 62. Compute flow values at lowest resolution and project to next level in the pyramid</li></ul>53<br />10/29/2009<br />
- 63. 54<br />Principal Component Analysis<br />Dimensionality Reduction Technique<br />Basic idea<br />Features in subspace provide more salient and richer information than the raw images themselves<br />Representation<br />Strain images represented as vector of weights of low dimensionality (feature vector)<br />Training<br />Learning these weights using a set of training images<br />Testing<br />Calculate distances to each of the training patterns in the projected subspace<br />54<br />10/29/2009<br />
- 64. 55<br />Steps involved in FEM<br />Discretization:problem domain is discretized into a collection of simple shapes, or elements; the continuous equations are discretized as finite differences and summations (instead of integrals and derivatives)<br />Assembly: The element equations for each element in the FEM mesh are assembled into a set of global equations that model the properties of the entire system<br />Application of Boundary Conditions: They reflect the known values for certain primary unknowns<br />Solve for Primary Unknowns: Modified global equations are solved for the primary unknowns at the nodes; interpolate for values between nodes<br />7<br />8<br />5<br />6<br />solve<br />discretize<br />object<br />u<br />3<br />4<br />2<br />1<br />+<br />global model<br />interpolate values between nodes<br />nodal mesh + local model<br />55<br />10/29/2009<br />
- 65. 56<br />Genetic Algorithm<br />[Start] Generate random population of n chromosomes<br />[Fitness] Evaluate the fitness function value of each chromosome in population<br />[New Population] Create new population by repeating following steps:<br />[Selection] Select 2 parent chromosomes from population<br />[Crossover] Crossover parents with some probability to form new offspring<br />[Mutation] Mutate offspring at each position with some probability<br />[Accepting] Place the new offspring in population<br />[Replace] Use the new generated population for a further run of the algorithm<br />[Test] If the end condition is satisfied, stop, and return the best solution in current population<br />56<br />10/29/2009<br />
- 66. 57<br />Road Map of Research<br />2004<br />2005<br />2006<br />2007<br />2008<br />2009<br />Performance Evaluation of Object Detection & Tracking Systems<br />FDM-Based Method: Profile Faces<br />Range Images/2D Images/Video: Performance of Strain<br />FEM-Based Method Frontal Faces<br />Modeling Material Constants:Expression Invariant Matching<br />Registration of Temporal MammogramsFinite Element Modeling<br />Defense!<br />57<br />10/29/2009<br />

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