Confluence of Visual Computing & Sparse Representation Yi Ma Electrical and Computer Engineering, UIUC & Visual Computing Group, MSRA CVPR, June 19th, 2009 CONTEXT - Massive High-Dimensional Data Recognition Surveillance Search and Ranking Bioinformatics The curse of dimensionality: …increasingly demand inference with limited samples for very highdimensional data. The blessing of dimensionality: … real data highly concentrate on low-dimensional, sparse, or degenerate structures in the high-dimensional space. But nothing is free: Gross errors and irrelevant measurements are now ubiquitous in massive cheap data. CONTEXT - New Phenomena with High-Dimensional Data KEY CHALLENGE: efficiently and reliably recover sparse or degenerate structures from high-dimensional data, despite gross observation errors. A sobering message: human intuition is severely limited in highdimensional spaces: Gaussian samples in 2D As dimension grows proportionally with the number of samples… A new regime of geometry, statistics, and computation… CONTEXT - High-dimensional Geometry, Statistics, Computation Exciting confluence of Analytical Tools: • Powerful tools from high-dimensional geometry, measure concentration, combinatorics, coding theory … Computational Tools: • Linear programming, convex optimization, greedy pursuit, boosting, parallel processing … Practical Applications: • Compressive sensing, sketching, sampling, audio, image, video, bioinformatics, classification, recognition … THIS TALK - Outline PART I: Face recognition as sparse representation Striking robustness to corruption PART II: From sparse to dense error correction How is such good face recognition performance possible? PART III: A practical face recognition system Alignment, illumination, scalability PART IV: Extensions, other applications, and future directions Part I: Key Ideas and Application Robust Face Recognition via Sparse Representation CONTEXT – Face recognition: hopes and high-profile failures # Pentagon Makes Rush Order for Anti-Terror Technology. Washington Post, Oct. 26, 2001. # Boston Airport to Test Face Recognition System. CNN.com, Oct. 26, 2001. # Facial Recognition Technology Approved at Va. Beach. 13News (wvec.com), Nov. 13, 2001. # ACLU: Face-Recognition Systems Won't Work. ZDNet, Nov. 2, 2001. # ACLU Warns of Face Recognition Pitfalls. Newsbytes, Nov. 2, 2001. # Identix, Visionics Double Up. CNN / Money Magazine, Feb. 22, 2002. # 'Face testing' at Logan is found lacking. Boston Globe, July 17, 2002. # Reliability of face scan technology in dispute. Boston Globe, August 5, 2002. # Tampa drops face-recognition system. CNET, August 21, 2003. # Airport anti-terror systems flub tests. USA Today, September 2, 2003. # Anti-terror face recognition system flunks tests. The Register, September 3, 2003. # Passport ID technology has high error rate. The Washington Post, August 6, 2004. # Smiling Germans ruin biometric passport system. VNUNet, November 10, 2005. # U.K. cops look into face-recognition tech. ZDNet News, January 17, 2006. # Police build national mugshot database. Silicon.com, January 16, 2006. # Face Recognition Algorithms Surpass Humans matching faces, PAMI, 2007. # 100% Accuracy in Automatic Face Recognition, Science, 2008., January 25, 2008 and the drama goes on and on… FORMULATION – Face recognition under varying illumination Face Subspaces Training Images Images of the same face under varying illumination lie approximately on a low (nine)-dimensional subspace, known as the harmonic plane [Basri & Jacobs, PAMI, 2003]. FORMULATION – Face recognition as sparse representation Assumption: the test image, , linear combination of k training images, say , can be expressed as a of the same subject: The solution, , , should be a sparse vector — of its entries should be zero, except for the ones associated with the correct subject. ROBUST RECOGNITION – Occlusion + varying illumination ROBUST RECOGNITION – Occlusion and Corruption ROBUST RECOGNITION – Properties of the Occlusion Several characteristics of occlusion : Randomly supported errors (location is unknown and unpredictable) Gross errors (arbitrarily large in magnitude) Sparse errors? (concentrated on relatively small part(s) of the image) ROBUST RECOGNITION – Problem Formulation Problem: Find the correct (sparse) solution from the corrupted and overdetermined system of linear equations: Conventionally, the minimum 2-norm (least squares) solution is used: ROBUST RECOGNITION – Joint Sparsity Thus, we are looking for a sparse solution system of linear equations to an under-determined : The problem can be solved efficiently via Linear Programming, and the solution is stable under moderate noise [Candes & Tao’04, Donoho’04]. The equivalence holds iff . Wright, Yang, Ganesh, Sastry, and Ma. Robust Face Recognition via Sparse Representation, PAMI 2009 ROBUST RECOGNITION – Geometric Interpretation Face recognition as determining which facet of the polytope the test image belongs to. Wright, Yang, Ganesh, Sastry, and Ma. Robust Face Recognition via Sparse Representation, PAMI 2009 ROBUST RECOGNITION - L1 versus L2 Solution Input: Wright, Yang, Ganesh, Sastry, and Ma. Robust Face Recognition via Sparse Representation, PAMI 2009 ROBUST RECOGNITION – Classification from Coefficients 123… subject 1… N subject i 123… subject n N subject i Classification criterion: assign to the class with the smallest residual. Wright, Yang, Ganesh, Sastry, and Ma. Robust Face Recognition via Sparse Representation, PAMI 2009 ROBUST RECOGNITION – Algorithm Summary Wright, Yang, Ganesh, Sastry, and Ma. Robust Face Recognition via Sparse Representation, PAMI 2009 EXPERIMENTS – Varying Level of Random Corruption Extended Yale B Database (38 subjects) Training: subsets 1 and 2 (717 images) Testing: subset 3 (453 images) 30% corruption 99.3% 90.7% 50% 37.5% 70% Wright, Yang, Ganesh, Sastry, and Ma. Robust Face Recognition via Sparse Representation, PAMI 2009 EXPERIMENTS – Varying Levels of Contiguous Occlusion Extended Yale B Database (38 subjects) Training: subsets 1 and 2 (717 images), EBP ~ 13.3%. Testing: subset 3 (453 images) 98.5% 90.3% 65.3% 30% occlusion Wright, Yang, Ganesh, Sastry, and Ma. Robust Face Recognition via Sparse Representation, PAMI 2009 EXPERIMENTS – Recognition with Face Parts Occluded Results corroborate findings in human vision: the eyebrow or eye region is most informative for recognition [Sinha’06]. However, the difference is less significant for our algorithm than for humans. Wright, Yang, Ganesh, Sastry, and Ma. Robust Face Recognition via Sparse Representation, PAMI 2009 EXPERIMENTS – Recognition with Disguises The AR Database (100 subjects) Training: 799 images (un-occluded) EBP = 11.6%. Testing: 200 images (with glasses) 200 images (with scarf) Wright, Yang, Ganesh, Sastry, and Ma. Robust Face Recognition via Sparse Representation, PAMI 2009 Part II: Theory Inspired by Face Recognition Dense Error Correction via L1 Minimization PRIOR WORK - Face Recognition as Sparse Representation Represent any test image wrt the entire training set as Test image Training dictionary coefficients corruption, occlusion Solution is not but only supported on images of the same subject should be unique sparse:… ideally, expected to be sparse: occlusion only affects a subset of the pixels Seek the sparsest solution: convex relaxation PRIOR WORK - Striking Robustness to Random Corruption Behavior under varying levels of random pixel corruption: Recognition rate 99.3% 90.7% 37.5% Can existing theory explain this phenomenon? PRIOR WORK - Error Correction by minimization Candes and Tao [IT ‘05]: • Apply parity check matrix s.t. , yielding Underdetermined system in sparse e only • Set • Recover from clean system PRIOR WORK - Error Correction by minimization Candes and Tao [IT ‘05]: • Apply parity check matrix s.t. , yielding Underdetermined system in sparse e only • Set • Recover from clean system Succeeds whenever in the reduced system . PRIOR WORK - Error Correction by minimization Candes and Tao [IT ‘05]: • Apply parity check matrix s.t. , yielding Underdetermined system in sparse e only • Set • Recover from clean system Succeeds whenever This work: in the reduced system • Instead solve Can be applied when A is wide (no parity check). . PRIOR WORK - Error Correction by minimization Candes and Tao [IT ‘05]: • Apply parity check matrix s.t. , yielding Underdetermined system in sparse e only • Set • Recover from clean system Succeeds whenever This work: in the reduced system . • Instead solve Succeeds whenever in the expanded system . PRIOR WORK - Equivalence in Algebraic sufficient conditions: • (In)-coherence Gribvonel + Nielsen ‘03 Donoho + Elad ‘03 suffices. • Restricted Isometry Candes + Tao ‘05 Candes + Tao + Romberg ‘06 suffices. “The columns of should be uniformly well-spread” FACE IMAGES - Contrast with Existing Theory Face images Highly coherent ( volume ) Image space very sparse: # images per subject, often nonnegative (illumination cone models). as dense as possible: robust to highest possible corruption. Existing theory: should not succeed. Wright, and Ma. ICASSP 2009, submitted to IEEE Trans. Information Theory. SIMULATION - Dense Error Correction? As dimension , an even more striking phenomenon emerges: Wright, and Ma. ICASSP 2009, submitted to IEEE Trans. Information Theory. SIMULATION - Dense Error Correction? As dimension , an even more striking phenomenon emerges: Wright, and Ma. ICASSP 2009, submitted to IEEE Trans. Information Theory. SIMULATION - Dense Error Correction? As dimension , an even more striking phenomenon emerges: Wright, and Ma. ICASSP 2009, submitted to IEEE Trans. Information Theory. SIMULATION - Dense Error Correction? As dimension , an even more striking phenomenon emerges: Wright, and Ma. ICASSP 2009, submitted to IEEE Trans. Information Theory. SIMULATION - Dense Error Correction? As dimension , an even more striking phenomenon emerges: Wright, and Ma. ICASSP 2009, submitted to IEEE Trans. Information Theory. SIMULATION - Dense Error Correction? As dimension , an even more striking phenomenon emerges: Conjecture: If the matrices are sufficiently coherent, then for any error fraction , as , solving corrects almost any error with . Wright, and Ma. ICASSP 2009, submitted to IEEE Trans. Information Theory. DATA MODEL - Cross-and-Bouquet Our model for should capture the fact that the columns are tightly clustered around a common mean : Face images L^-norm of deviations wellcontrolled ( -> v ) Image space Mean is mostly incoherent with standard (error) basis We call this the “Cross-and-Bouquet’’ (CAB) model. Wright, and Ma. ICASSP 2009, submitted to IEEE Trans. Information Theory. ASYMPTOTIC SETTING - Weak Proportional Growth • Observation dimension • Problem size grows proportionally: • Error support grows proportionally: • Support size sublinear in Sublinear growth of Need at least : is necessary to correct arbitrary fractions of errors: “clean” equations. Wright, and Ma. ICASSP 2009, submitted to IEEE Trans. Information Theory. MAIN RESULT - Correction of Arbitrary Error Fractions Recall notation: “ recovers any sparse signal from almost any error with density less than 1” Wright, and Ma. ICASSP 2009, submitted to IEEE Trans. Information Theory. SIMULATION - Comparison to Alternative Approaches “L1 - [A I]”: “L1 - comp”: “ROMP”: Candes + Tao ‘05 Regularized orthogonal matching pursuit Needell + Vershynin ‘08 SIMULATION - Arbitrary Errors in WPG Fraction of correct successes for increasing m ( , ) Wright, and Ma. ICASSP 2009, submitted to IEEE Trans. Information Theory. IMPLICATIONS (1) - Error Correction with Real Faces For real face images, weak proportional growth corresponds to the setting where the total image resolution grows proportionally to the size of the database. Fraction of correct recoveries Above: corrupted images. ( 50% probability of correct recovery ) Below: reconstruction. Wright, and Ma. ICASSP 2009, submitted to IEEE Trans. Information Theory. IMPLICATIONS (2) – Verification via Sparsity Valid Subject Invalid Subject Reject as invalid if Sparsity Concentration Index Wright, Yang, Ganesh, Sastry, and Ma. Robust Face Recognition via Sparse Representation, PAMI 2009 IMPLICATIONS (2) – Receiver Operating Characteristic (ROC) Yale Extended B, 19 valid subjects, 19 invalid, under different levels of occlusions: 0% 10% 20% 30% 50% Wright, Yang, Ganesh, Sastry, and Ma. Robust Face Recognition via Sparse Representation, PAMI 2009 IMPLICATIONS (3) - Communications through Bad Channels Receiver Transmitter Extremely corrupting channel Transmitter encodes message as Receiver observes corrupted version linear programming. . , recovers by Wright, and Ma. ICASSP 2009, submitted to IEEE Trans. Information Theory. IMPLICATIONS (4) - Application to Information Hiding Alice Bob Intentionally corrupts messages ????????? Knows , can recover by linear programming Eavesdropper Code breaking as a dictionary learning problem… Wright, and Ma. ICASSP 2009, submitted to IEEE Trans. Information Theory. Part III: A Practical Automatic Face Recognition System FACE RECOGNITION – Toward a Robust, Real-World System So far: surprisingly good laboratory results, strong theoretical foundations. Remaining obstacles to truly practical automatic face recognition: • Pose and misalignment - real face detector imprecision! • Obtaining sufficient training - which illuminations are truly needed? • Scalability to large databases - both in speed and accuracy. All three difficulties can be addressed within the same unified framework of sparse representation. FACE RECOGNITION – Coupled Problems of Pose and Illumination Sufficient training illuminations, but no explicit alignment: Alignment corrected, but insufficient training illuminations: FACE RECOGNITION – Coupled Problems of Pose and Illumination Sufficient training illuminations, but no explicit alignment: Alignment corrected, but insufficient training illuminations: Robust alignment and training set selection: Recognition succeeds ROBUST POSE AND ALIGNMENT – Problem Formulation What if the input image is misaligned, or has some pose? If were known, still have a sparse representation Seek the that gives the sparsest representation: Wagner, Wright, Ganesh, Zhou and Ma. To appear in CVPR 09 POSE AND ALIGNMENT – Iterative Linear Programming Robust alignment as sparse representation: Nonconvex in Linearize about current estimate of : Linear program Solve, set Wagner, Wright, Ganesh, Zhou and Ma. To appear in CVPR 09 POSE AND ALIGNMENT – How well does it work? Succeeds up to >45o of pose:: Succeeds up to translations of 20% of face width, up to 30o in-plane rotation:: Recognition rate for synthetic misalignments (Multi-PIE) Wagner, Wright, Ganesh, Zhou and Ma. To appear in CVPR 09 POSE AND ALIGNMENT – L1 vs L2 solutions Crucial role of sparsity in robust alignment: Minimum -norm solution Least-squares solution Wagner, Wright, Ganesh, Zhou and Ma. To appear in CVPR 09 POSE AND ALIGNMENT – Algorithm details • First align to each subject separately Efficient multi-scale implementation • Select k subjects with smallest global sparse representation , classify based on Excellent classification, validation and robustness with a linear-time algorithm that is efficient in practice and highly parallelizable. Wagner, Wright, Ganesh, Zhou and Ma. To appear in CVPR 09 LARGE-SCALE EXPERIMENTS – Multi-PIE Database Training: 249 subjects appearing in Session 1, 9 illuminations per subject. Testing: 336 subjects appearing in Sessions 2,3,4. All 18 illuminations. Examples of failures: Drastic changes in personal appearance over time Wagner, Wright, Ganesh, Zhou and Ma. To appear in CVPR 09 LARGE-SCALE EXPERIMENTS – Multi-PIE Database Training: 249 subjects appearing in Session 1, 9 illuminations per subject. Testing: 336 subjects appearing in Sessions 2,3,4. All 18 illuminations. Receiver Operating Characteristic (ROC) Validation performance: Is the subject in the database of 249 people? NN, NS, LDA not much better than chance. Our method achieves an equal error rate of < 10%. Wagner, Wright, Ganesh, Zhou and Ma. To appear in CVPR 09 FACE RECOGNITION – Coupled Problems of Pose and Illumination Sufficient training illuminations, but no explicit alignment: Alignment corrected, but insufficient training illuminations: Robust alignment and training set selection: Recognition succeeds ACQUISITION SYSTEM – Efficient training collection Generate different illuminations by reflecting light from DLP projectors off walls, onto subject: Fast: hundreds of images in a matter of seconds, flexible and easy to assemble. Wagner, Wright, Ganesh, Zhou and Ma. To appear in CVPR 09 WHICH ILLUMINATIONS ARE NEEDED? Real data representation error as a function of… … Coverage of the sphere Rear illuminations! Granularity of the partition 32 illumination cells • Rear illuminations are critical for representing real world variability Missing from standard data sets such as AR, PIE, MultiPIE! • 30-40 distinct illumination patterns suffice Wagner, Wright, Ganesh, Zhou and Ma. To appear in CVPR 09 REAL-WORLD EXPERIMENTS – Our Dataset Sufficient set of 38 training illuminations: Recognition performance over 74 subjects: Subset 1 95.9% rec. rate Subset 2 91.5% rec. rate Subset 3 62.3% rec. rate Subset 4 73.7% rec. rate Subset 5 53.5% rec. rate Wagner, Wright, Ganesh, Zhou and Ma. To appear in CVPR 09 Part IV: Extensions, Other Applications, and Future Directions EXTENSIONS (1) – Topological Sparse Solutions Recognition rate 99.3% 90.7% 37.5% 98.5% 90.3% 65.3% EXTENSIONS (1) – Topological Sparse Solutions How to better exploit the spatial characteristics of the error e in face recognition? Simple solution: Markov random field and L1 minimization. 60% occlusion Query image recovered error support recovered error recovered image Longer-term direction: Sparse representation on structured domains (ala [Baraniuk ’08, Do ’07]): Z. Zhou, A. Wagner, J. Wright, and Ma. Submitted to ICCV09. EXTENSIONS (2) – Does Feature Selection Matter? 12x10 pixels 120 dim 120 dim Wright, Yang, Ganesh, Sastry, and Ma. Robust Face Recognition via Sparse Representation, PAMI 2009 EXTENSIONS (2) – Does Feature Selection Matter? Compressed sensing: – Number of linear measurements is more important than specific details of how those measurements are taken. – d > 2k log (N/d) random measurements suffice to efficiently reconstruct any k-sparse signal. [Donoho and Tanner ’07] Wright, Yang, Ganesh, Sastry, and Ma. Robust Face Recognition via Sparse Representation, PAMI 2009 EXTENSIONS (2) – Does Feature Selection Matter? Extended Yale B: 38 subjects, 2,414 images of size 192x168 Training: 1,207 random images, Testing: remaining 1,207 images Wright, Yang, Ganesh, Sastry, and Ma. Robust Face Recognition via Sparse Representation, PAMI 2009 OTHER APPLICATIONS (1) - Image Super-resolution Enhance images by sparse representation in coupled dictionaries (high- and low-resolution) of image patches: MRF / BP [Freeman IJCV ‘00] Soft edge prior [Dai ICCV ‘07] Our method Original Original J. Yang, Wright, Huang, and Ma. CVPR 2008 OTHER APPLICATIONS (2) - Face Hallucination J. Yang, H. Tangt, Huang, and Ma. ICIP 2008 OTHER APPLICATIONS (3) - Activity Detection & Recognition Precision: 98.8% and recall: 94.2%, far better than other existing detectors & classifiers A. Yang et. al. (at UC Berkeley). CVPR 2008 OTHER APPLICATIONS (4) - Robust Motion Segmentation deals with incomplete or mistracked features with dataset 80% corrupted! S. Rao, R. Tron, R. Vidal, and Ma. CVPR 2008 OTHER APPLICATIONS (5) - Data Imputation in Speech 91% at SNR -5dB on AURORA-2 compared to 61% with conventional… J.F. Gemmeke and G. Cranen, EUSIPCO’08 FUTURE WORK (1) – High-Dimensional Pattern Recognition Toward an understanding of high-dimensional pattern classification… Data tasks beyond error correction: Excellent classification performance even with high-coherent dictionary Excellent validation behavior based on sparsity of the solution Understanding either behavior requires a much more expressive model for “what happens inside the bouquet?” FUTURE WORK (2) – From Sparse Vectors to Low-Rank Matrices … … D - observation A – low-rank … E – sparse error Robust PCA Problem: given D, recover A. convex relaxation Nuclear norm Wright, Ganesh, Rao and Ma, submitted to the Journal of the ACM. ROBUST PCA – Which matrices and which errors? Random orthogonal model (of rank r) [Candes & Recht ‘08]: independent samples from invariant measure on Steifel manifold of orthobases of rank r. arbitrary. Bernoulli error signs-and-support (with parameter Magnitude of ): is arbitrary. Wright, Ganesh, Rao and Ma, submitted to the Journal of the ACM. MAIN RESULT – Exact Solution of Robust PCA “Convex optimization recovers almost any matrix of rank O(m/log m) from errors affecting O(m2) of the observations!” Wright, Ganesh, Rao and Ma, submitted to the Journal of the ACM. ROBUST PCA – Contrast with literature • [Chandrasekharan et. al. 2009]: Correct recovery whp for Only guarantees recovery from vanishing fractions of errors, even when r = O(1). • This work: Correct recovery whp for , even with Key technique: Iterative surgery for producing a certifying dual vector (extends [Wright and Ma ’08]). Wright, Ganesh, Rao and Ma, submitted to the Journal of the ACM. BONUS RESULT – Matrix completion in proportional growth “Convex optimization exactly recovers matrices of rank O(m), even when O(m2) entries are missing!” Wright, Ganesh, Rao and Ma, submitted to the Journal of the ACM. MATRIX COMPLETION – Contrast with literature • [Candes and Tao 2009]: Correct completion whp for Empty for • This work: Correct completion whp for , even with Exploits rich regularity and independence in random orthogonal model. Caveats: - [C-T ‘09] tighter for small r. - [C-T ‘09] generalizes better to other matrix ensembles. Wright, Ganesh, Rao and Ma, submitted to the Journal of the ACM. FUTURE WORK (2) – Robust PCA via Iterative Thresholding ? Efficient solutions to Semidefinite program in millions of unknowns! Shrink singular values repeat Shrink absolute values Provable (and efficient) convergence to global optimum. Future direction: sampling approximations to the singular value thresholding operator [Rudelson and Vershynin ’08] ? Wright, Ganesh, Rao and Ma, submitted to the Journal of the ACM. FUTURE WORK (2) - Video Coding and Anomaly Detection Videos are highly coherent data. Errors correspond to pixels that cannot be well interpolated by the previous video. 550 frames, 64 x 80 pixels, Video Low-rank appx. Sparse error significant illumination variation Background variation Anomalous activity Wright, Ganesh, Rao and Ma, submitted to the Journal of the ACM. FUTURE WORK (2) - Background modeling Static camera surveillance video Video Low-rank appx. Sparse error 200 frames, 72 x 88 pixels, Significant foreground motion Wright, Ganesh, Rao and Ma, submitted to the Journal of the ACM. FUTURE WORK (2) - Face under different illuminations Original images Low-rank appx. Sparse error Ext. Yale B database, 29 images of one subject. Images are 96 x 84 pixels. Wright, Ganesh, Rao and Ma, submitted to the Journal of the ACM. CONCLUSIONS Analytic and algorithmic tools from sparse representation lead to a new approach in face recognition: • Robustness to corruption and occlusion • Performance exceeds expectation & human ability Face recognition reveals new phenomena in high-dim statistics & geometry: • Dense error correction with a coherent dictionary • Recovery of corrupt low-rank matrices Theoretical insights to mathematical models lead back to practical gains • Robust to misalignment, illumination, and occlusion • Scalable in both computation and performance in realistic scenarios MANY NEW APPLICATIONS BEYOND FACE RECOGNITION… REFERENCES + ACKNOWLEDGEMENT - Robust Face Recognition via Sparse Representation IEEE Trans. on Pattern Analysis and Machine Intelligence, February 2009. - Dense Error Correction via L1-minimization ICASSP 2008, Submitted to IEEE Trans. Information Theory, September 2008. - Towards a Practical Face Recognition System: Robust Alignment and Illumination via Sparse Representation IEEE Conference on Computer Vision and Pattern Recognition, June 2009. - Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices by Convex Optimization Submitted to the Journal of the ACM, May 2009. John Wright, Allen Yang, Andrew Wagner, Arvind Ganesh, Zihan Zhou This work was funded by NSF, ONR, and MSR Yi Ma – Confluence of Computer Vision and Sparse Representation THANK YOU Questions, please? Yi Ma – Confluence of Computer Vision and Sparse Representation Yi Ma – Confluence of Computer Vision and Sparse Representation EXPERIMENTS – Design of Robust Training Sets The Equivalence Breakdown Point Extended Yale B AR Database Bounding EBP, submitted to ACC ‘09, Sharon, Wright, and Ma FEATURE SELECTION – Extended Yale B Database 38 subjects, 2,414 images of size 192x168 Training: 1,207 random images, Testing: remaining 1,207 images L1 Dimension (d) 30 56 120 504 Eigen [%] 80.0 89.6 94.0 97.0 Laplacian [%] 80.6 91.7 93.9 96.5 Random[%] 81.9 90.8 95.0 96.8 Downsample[%] 76.2 87.6 92.7 96.9 Fisher[%] 85.9 N/A N/A N/A Nearest Subspace Nearest Neighbor Dimension (d) 30 56 120 504 Dimension (d) 30 56 120 504 Eigen [%] 72.0 79.8 83.9 85.8 Eigen [%] 89.9 91.1 92.5 93.2 Laplacian [%] 75.6 81.3 85.2 87.7 Laplacian [%] 89.0 90.4 91.9 93.4 Random[%] 60.1 66.5 67.8 66.4 Random[%] 87.4 91.5 93.9 94.1 Downsample[%] 46.7 54.7 61.8 65.4 Downsample[%] 80.8 88.2 91.1 93.4 Fisher[%] 87.7 N/A N/A N/A Fisher[%] 81.9 N/A N/A N/A FEATURE SELECTION – AR Database 100 subjects, 1,400 images of size 165x120 Training: 700 images, varying lighting, expression Testing: 700 images from second session FEATURE SELECTION – AR Database 100 subjects, 1,400 images of size 165x120 Training: 700 images, varying lighting, expression Testing: 700 images from second session L1 Dimension (d) 30 56 120 504 Eigen [%] 71.1 80.0 85.7 92.0 Laplacian [%] 73.7 84.7 91.0 94.3 Random[%] 57.8 75.5 87.5 94.7 Downsample[%] 46.8 67.0 84.6 93.9 Fisher[%] 87.0 92.3 N/A N/A Nearest Neighbor Nearest Subspace Dimension (d) 30 56 120 504 Dimension (d) 30 56 120 504 Eigen [%] 68.1 74.8 79.3 80.5 Eigen [%] 64.1 77.1 82.0 85.1 Laplacian [%] 73.1 77.1 83.8 89.7 Laplacian [%] 66.0 77.5 84.3 90.3 Random[%] 56.7 63.7 71.4 75.0 Random[%] 59.2 68.2 80.0 83.3 Downsample[%] 51.7 60.9 69.2 73.7 Downsample[%] 56.2 67.7 77.0 82.1 Fisher[%] 83.4 86.8 N/A N/A Fisher[%] 80.3 85.8 N/A N/A FEATURE SELECTION – Recognition with Face Parts Feature Masks Examples of Test Features Features nose right eye mouch & chin Dimension 4,270 5,050 12,936 L1 87.3% 93.7% 98.3% NN 49.2% 68.8% 72.7% NS 83.7% 78.6% 94.4% SVM 70.8% 85.8% 95.3% NOTATION - Correct Recovery of Solutions Whether Call is recovered depends only on -recoverable if and the minimizer is unique. with these signs and support PROOF (1) - Problem Geometry Consider a fixed . W.l.o.g., let Success iff Restrict to and write With some manipulation, optimality condition becomes PROOF (1) - Problem Geometry Consider a fixed . W.l.o.g., let Success iff Restrict to and write With some manipulation, optimality condition becomes PROOF (1) - Problem Geometry Introduce The NSC hyperplane and the unit ball of are disjoint. PROOF (1) - Problem Geometry Introduce The NSC hyperplane and the unit ball of are disjoint. PROOF (1) - Problem Geometry Introduce The NSC hyperplane and the unit ball of are disjoint. PROOF (1) - Problem Geometry is a complicated polytope. Instead look for a hyperplane separating and in the higher-dimensional space. PROOF (2) - When Does the Iteration Succeed? Lemma: success if Proof: want to show Consider the three statements: PROOF (2) - When Does the Iteration Succeed? Lemma: success if Proof: want to show Consider the three statements: Base case: Trivial Use that PROOF (2) - When Does the Iteration Succeed? Lemma: success if Proof: want to show Consider the three statements: Inductive step: PROOF (2) - When Does the Iteration Succeed? Lemma: success if Proof: want to show Consider the three statements: Inductive step (cont’d): Magnitude PROOF (2) - When Does the Iteration Succeed? Lemma: success if Proof: want to show Consider the three statements: Inductive step (cont’d):