Computational and Performance Aspects of PCA-Based Face-Recognition Algorithms

By D. Electrical, National Institute of. Perception, 2001

577 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/f095554be869d80ff273f438875ecad391bfdae0

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Citations in the corpus

Learning Continual Compatible Representation for Re-indexing Free Lifelong Person Re-identification (2024, methodology): “We employ mean Average Precision (mAP) [41] and Rank@1 accuracy (R@1) [15] to evaluate our method on each datasets.”
Shape and Appearance Context Modeling (2007, background): “Identification results are reported in terms of cumulative match characteristic (CMC) curves [15], which tell the rate at which the correct match is within the k-nearest neighbors, with k that varies from 1 to 20.”
Color Invariants for Person Reidentification (2013, methodology): “The CMC is a curve plotting the probability of the correct match being in the first n top-ranked matches, for every n.”
Local Descriptors Encoded by Fisher Vectors for Person Re-identification (2012, methodology): “For both datasets, the performance is measured by the Cumulative Matching Characteristic (CMC) curve [24], which is the standard metric for person re-identiﬁcation.”
Preserving privacy by de-identifying face images (2005, unknown): “software [11].”
Introduction to Biometrics (2011, methodology): “Here, we assume that the question being asked is, \Does the top match correspond to the correct identity?" An alternate question could be, \Does any one of the top k matches correspond to the correct identity?" (see [ 23 ]).”
From Template to Image: Reconstructing Fingerprints from Minutiae Points (2007, background): “The CMC graph plots the identification rate as a function of the number of top matches (ranks) [ 25 ].”
Face identification across different poses and illuminations with a 3D morphable model (2002, unknown): “[11]) to use O ; / d ( a d ( « d ( d ( ! F " 8 ; ; 8 ; ; 8 F 8 ; ; 8 8 =”
Face recognition: A literature survey (2003, methodology): “The vector comparison varies in different implementations and can influence the system’s performance dramatically [Moon and Phillips 2001].”
Overview of the face recognition grand challenge (2005, methodology): “Our implementation is based on Moon and Phillips [5] and Beveridge et al [6].”
An Algorithm for the Principal Component Analysis of Large Data Sets (2010, unknown): “The denoising is clearly satisfactory.”
FRVT 2006 and ICE 2006 large-scale results (2007, unknown): “The high-resolution images were taken with a 4 Megapixel Canon PowerShot G2.”
MPCA: Multilinear Principal Component Analysis of Tensor Objects (2008, methodology): “If is the feature vector, and , which is the simplified Mahalanobis distance introduced in [39].”
RGB-Infrared Cross-Modality Person Re-identification (2017, methodology): “Notice that, for CMC under multi-shot setting, only the maximum similarity in all gallery images of the same person is taken to compute the rank list.”
Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification (2016, methodology): “As a result, the final CMC top-1 accuracies are increased by several percents, as listed in the last row of Table 3.”
Face Recognition Based on Fitting a 3D Morphable Model (2003, background): “An alternative measure for similarity is the cosine of the angle between two vectors [6], [27]: dA 1⁄4 c1;c2 h i c1 k k c2 k k .”