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Transform-Invariant Sparse Representation for Simultaneous Face Alignment and Recognition

This study presents a novel approach combining face alignment and recognition using Transform-Invariant Sparse Representation (TSR). Unlike traditional methods, TSR effectively handles misaligned test images and dynamic texture registration, optimizing solutions in one framework. TSR-based online registration leverages sparse representation constancy for aligning new frames with preceding ones, enhancing face identification accuracy. Experimental results on face and dynamic scene data demonstrate improved performance compared to conventional approaches.

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Transform-Invariant Sparse Representation for Simultaneous Face Alignment and Recognition

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  1. Simultaneous Image Transformation and Sparse Representation Recovery Junzhou Huang1, XiaoleiHuang2,Dimitris Metaxas1 1 Division of Computer and Information Sciences, Rutgers University, NJ, USA 2 Department of Computer Science and Engineering, Lehigh University, PA, USA Experiments on Face Data • Randomface • : test image; : sparse indicator for identity • Face recognition only and can handle the misaligned test image • Proposed TSR based approach • : test image; : sparse indicator for identity; : alignment parameter • Simultaneous face alignment and recognition as we can recover both and • Traditional video registration approach • Brightness constancy assumption • Cannot handle dynamic texture registration • Previous Dynamic texture approach • Registration and dynamic texture representation are separated • Cannot optimize the solution in one framework and thus the solutions are sub-optimal • not online • TSR based online registration • sparse representation constancy assumption • Given a new frame, its aligned version should be sparsely represented by the preceding frames • : the incoming new image frame; : sparse indicator based on preceding frames; : the registration parameter; : the measurement matrix based on preceding image frames TSR Based Face Alignment&Recognition • : linear measurements • : sparse signals • : measurement matrix • Sparse representation theory • Effectively reconstruct sparse signal x with measurements as few as possible • Geometry transformation problem? • : linear measurements • : sparse signals • : measurement matrix • : transformed version of y with parameter • Our solutions • Different measurement matrix, for example • Iteratively random manifold projection • Extended Yale B database • Moving flower bed sequence • 2.09% FEF; 5 seconds in MATLAB Sparse Representation Identification TSR Based Dynamic Texture Registration Transform-invariant Sparse Representation Figure. (a) Training images; (b) Test images; (c) Randomfaces[21]; (d) Proposed Verfication Experiments on Dynamic Scene Table. FEF of horizontal cumulative motion (Escalator Sequence) Figure. Cumulative motion; Left: horizontal; Right: Vertical

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