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article

Recognizing faces with PCA and ICA

Authors:

Bruce A. Draper,

Marian Stewart Bartlett,

J. Ross BeveridgeAuthors Info & Claims

Computer Vision and Image Understanding, Volume 91, Issue 1-2

Pages 115 - 137

https://doi.org/10.1016/S1077-3142(03)00077-8

Published: 01 July 2003 Publication History

Abstract

This paper compares principal component analysis (PCA) and independent component analysis (ICA) in the context of a baseline face recognition system, a comparison motivated by contradictory claims in the literature. This paper shows how the relative performance of PCA and ICA depends on the task statement, the ICA architecture, the ICA algorithm, and (for PCA) the subspace distance metric. It then explores the space of PCA/ICA comparisons by systematically testing two ICA algorithms and two ICA architectures against PCA with four different distance measures on two tasks (facial identity and facial expression). In the process, this paper verifies the results of many of the previous comparisons in the literature, and relates them to each other and to this work. We are able to show that the FastICA algorithm configured according to ICA architecture II yields the highest performance for identifying faces, while the InfoMax algorithm configured according to ICA architecture II is better for recognizing facial actions. In both cases, PCA performs well but not as well as ICA.

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Index Terms

Recognizing faces with PCA and ICA
1. Computing methodologies
2. Theory of computation
  1. Design and analysis of algorithms

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Information & Contributors

Information

Published In

cover image Computer Vision and Image Understanding

Computer Vision and Image Understanding Volume 91, Issue 1-2

Special issue on Face recognition

July 2003

245 pages

ISSN:1077-3142

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Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 July 2003

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Cited By

Liang TShen QWang SChen YZhang GChen J(2024)Data Completion-Guided Unified Graph Learning for Incomplete Multi-View ClusteringACM Transactions on Knowledge Discovery from Data10.1145/366429018:8(1-23)Online publication date: 9-May-2024
https://dl.acm.org/doi/10.1145/3664290
Zhou HChen WLiu JCheng LXia M(2024)Trustworthy and intelligent fault diagnosis with effective denoising and evidential stacked GRU neural networkJournal of Intelligent Manufacturing10.1007/s10845-023-02221-135:7(3523-3542)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10845-023-02221-1
Li GGao QHan JGao X(2023)A Coarse-to-Fine Cell Division Approach for Hyperspectral Remote Sensing Image ClassificationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.333913534:6(4928-4941)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3339135
Kemiha MKacha A(2023)Single-Channel Blind Source Separation using Adaptive Mode Separation-Based Wavelet Transform and Density-Based Clustering with Sparse ReconstructionCircuits, Systems, and Signal Processing10.1007/s00034-023-02350-142:9(5338-5357)Online publication date: 19-Apr-2023
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Shang LZhang YHuang B(2023)Palmprint Recognition Utilizing Modified LNMF MethodAdvanced Intelligent Computing Technology and Applications10.1007/978-981-99-4742-3_31(377-388)Online publication date: 10-Aug-2023
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Arora MKumar M(2021)AutoFER: PCA and PSO based automatic facial emotion recognitionMultimedia Tools and Applications10.1007/s11042-020-09726-480:2(3039-3049)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s11042-020-09726-4
Dutta KBhattacharjee DNasipuri MKrejcar O(2021)Complement component face space for 3D face recognition from range imagesApplied Intelligence10.1007/s10489-020-02012-851:4(2500-2517)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s10489-020-02012-8
Benkhaira SLayeb A(2020)Face Recognition Using RLDA Method Based on Mutated Cuckoo Search Algorithm to Extract Optimal FeaturesInternational Journal of Applied Metaheuristic Computing10.4018/IJAMC.202004010611:2(118-133)Online publication date: 1-Apr-2020
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Han QSu JZhao Y(2019)More Adaptive and UpdatableJournal of Electrical and Computer Engineering10.1155/2019/83708352019Online publication date: 1-Jan-2019
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Rajendra Kurup AAjith MMartínez Ramón M(2019)Semi-supervised facial expression recognition using reduced spatial features and Deep Belief NetworksNeurocomputing10.1016/j.neucom.2019.08.029367:C(188-197)Online publication date: 20-Nov-2019
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