Principal Angles Separate Subject Illumination Spaces in YDB and CMU-PIE

Beveridge, J. Ross; Draper, Bruce A.; Chang, Jen-Mei; Kirby, Michael; Kley, Holger P.; Peterson, Chris

doi:10.1109/tpami.2008.200

Cited by 37 publications

(30 citation statements)

References 24 publications

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“…Without any assumption on data distribution, it has been shown in [29], [30] that these model-free representations inherit many favorable properties. Another advantage of nonparametric low-dimensional subspace/manifold image set representations is that they can model the illumination of faces very well [10], [21].…”

Section: Related Workmentioning

confidence: 99%

Face Recognition Using Sparse Approximated Nearest Points between Image Sets

Mian

Owens

2012

IEEE Trans. Pattern Anal. Mach. Intell.

181

146

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Abstract-We propose an efficient and robust solution for image set classification. A joint representation of an image set is proposed which includes the image samples of the set and their affine hull model. The model accounts for unseen appearances in the form of affine combinations of sample images. To calculate the between-set distance, we introduce the Sparse Approximated Nearest Point (SANP). SANPs are the nearest points of two image sets such that each point can be sparsely approximated by the image samples of its respective set. This novel sparse formulation enforces sparsity on the sample coefficients and jointly optimizes the nearest points as well as their sparse approximations. Unlike standard sparse coding, the data to be sparsely approximated are not fixed. A convex formulation is proposed to find the optimal SANPs between two sets and the accelerated proximal gradient method is adapted to efficiently solve this optimization. We also derive the kernel extension of the SANP and propose an algorithm for dynamically tuning the RBF kernel parameter while matching each pair of image sets. Comprehensive experiments on the UCSD/Honda, CMU MoBo, and YouTube Celebrities face datasets show that our method consistently outperforms the state of the art.

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Section: Related Workmentioning

confidence: 99%

Face Recognition Using Sparse Approximated Nearest Points between Image Sets

Mian

Owens

2012

IEEE Trans. Pattern Anal. Mach. Intell.

181

146

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“…The manifold is approximated by piecewise linear subspaces. Such manifold representations can model the illumination variations of faces very well [11], [30]. The piecewise linear models are estimated by clustering the set data into flat structures [37], [49] or a hierarchy [28], [36].…”

Section: Related Work a Image Set Classificationmentioning

confidence: 99%

Image Set Based Face Recognition Using Self-Regularized Non-Negative Coding and Adaptive Distance Metric Learning

Mian

Hu²,

Hartley

et al. 2013

IEEE Trans. on Image Process.

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Abstract-Simple nearest neighbor classification fails to exploit the additional information in image sets. We propose selfregularized non-negative coding to define between set distance for robust face recognition. Set distance is measured between the nearest set points (samples) that can be approximated from their orthogonal basis vectors as well as from the set samples under the respective constraints of self-regularization and non-negativity. Self-regularization constrains the orthogonal basis vectors to be similar to the approximated nearest point. The non-negativity constraint ensures that each nearest point is approximated from a positive linear combination of the set samples. Both constraints are formulated as a single convex optimization problem and the accelerated proximal gradient method with linear-time Euclidean projection is adapted to efficiently find the optimal nearest points between two image sets. Using the nearest points between a query set and all the gallery sets as well as the active samples used to approximate them, we learn a more discriminative Mahalanobis distance for robust face recognition. The proposed algorithm works independently of the chosen features and has been tested on gray pixel values and Local Binary Patterns (LBP). Experiments on three standard datasets show that the proposed method consistently outperforms existing state-of-the-art.

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“…For example, Beveridge et al 57 demonstrate for the popular CMU PIE face database 58 with variable illumination that the first principal angle θ i between sets of images of different people is sufficient to perfectly distinguish between all 67 people in the dataset. This result is based on using Eq.…”

Section: Illumination Cones Illumination Normalization and Relightingmentioning

confidence: 99%

Biometric face recognition: from classical statistics to future challenges

Givens

Beveridge

Lui

et al. 2013

WIREs Computational Stats

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Face recognition involves at least three major concepts from statistics: dimension reduction, feature extraction, and prediction. A selective review of algorithms, from seminal to state-of-the-art, explores how these concepts persist as organizing principles in the field. Algorithms based directly upon classical statistical techniques include linear methods like principal component analysis and linear discriminant analysis. Nonlinear manifold methods, such as Laplacianfaces and Stiefel quotients, offer considerable performance improvements. Other noteworthy ideas include three-dimensional morphable models, methods using local regions and/or alternative feature spaces (e.g., elastic bunch graph matching and local binary patterns) and sparse representation approaches. Opportunities for innovative statistical and collaborative research in face recognition are expanding in tandem with the growing complexity and diversity of applications.

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Principal Angles Separate Subject Illumination Spaces in YDB and CMU-PIE

Cited by 37 publications

References 24 publications

Face Recognition Using Sparse Approximated Nearest Points between Image Sets

Face Recognition Using Sparse Approximated Nearest Points between Image Sets

Image Set Based Face Recognition Using Self-Regularized Non-Negative Coding and Adaptive Distance Metric Learning

Biometric face recognition: from classical statistics to future challenges

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