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Face Recognition and Detection Using Eigenfaces
Based on: M.A. Turk and A. P. Pentland,“Face Recognition Using Eigenfaces,”Proc. IEEE Conf. on CVPR, Maui, HI, USA, pp , Jun Kohsia Huang ECE 285 Class Presentation Prof. Mohan Trivedi Winter, 2001 Department of Electrical and Computer Engineering University of California, San Diego
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Background Works Detecting individual face features
- Difficult to extend to non-frontal views - Insufficient representation for face identification Neural network approaches Multiresolution template matching Other methods: - A. Pentland and T. Choudhury, “Face Recognition for Smart Environments,” IEEE Comp. Mag., pp , Feb - P. Penev and J. Atick, “Local Feature Analysis: A General Statistical Theory for Object Representation,” Network: Compu. in Neural Syst. 7, pp , Mar
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Interpretations of Eigenface
Information Theory: Extract relevant information in face images, encode face images efficiently, and compare individual face images. Linear Algebra: Find principle components of the distribution of faces, which is the eigenvectors of the covariance matrix of the training faces. Principle components = Features = Eigenfaces.
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Eigenface Algorithm Dimension reduction: Face images can be represented as a linear combination of the eigenfaces. Approximation: The feature space or eigenface space can be approximated by the eigenfaces associated with the largest eigenvalues.
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Eigenface Example
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Procedure Initialization: Obtain training faces and calculate the eigenfaces. Operating: Calculate a set of weights by projecting the test face into eigenface space. Face detection: If the image is close to the face space, it is a face image. Recognition: If the test face is close to a certain training face, it is recognized.
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Formulation Eigenface Recognition Algorithm Training Face Vectors
Correlation Matrix Singular Value Decomposition Eigenface Representation
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Face Detection Face Space Error Projection Face Image The error is the difference between the original image and its projection image onto eigenface space. If the error is within a threshold, the image is detected as a face image. Efficient calculation available.
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Face Recognition Face Space Face Image Projection Error Class B Class A Class C If the projection of face image onto eigenface space is close to one training face, it is identified as that training face. Distance measure can be Euclidian distance.
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Classification Summary
Four possible patterns of an input image: Near face space and its projection is near a face class Recognized. Near face space but its projection is distant from all face classes Unknown face. Distant from face space but its projection is near a face class Not a face image. Distant from face space and its projection is distant from all face classes Not a face image.
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Compare with Projection Vectors of Training Faces
Implementation Projection into Eigenface Space Snapshot Image Processing & Face Extraction Projection Vector Face Image Compare with Projection Vectors of Training Faces Classification Person ID & Facing Angle Near Y N Not a Face Image
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Accuracy 2500 face images: Infinite thresholds: 96% correct on lighting variation, 85% on face orientation variation, 64% on size (zooming) variation. Limited thresholds: Adjust unknown rate to 20%, the above correct rates becomes 100%, 94%, and 74%, respectively. 25 face images: 74% correct rate for controlled conditions.
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Accuracy (Cont.) FERET Competition: Standardized testing criteria.
Not accurate enough for lower dimension eigenface spaces. Needs approximately 120 dimensions to compete with local feature analysis.
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