Face Recognition System By Arthur

Introduction  A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. --Wikipedia  Face perception  Machine Learning Example  (Microsoft)

Basic Face Recognition System

Step 1: Face detection  Identify and locate human faces  Normalization Face detection methods:  Knowledge-based methods  Feature invariant approaches  Template matching methods  Appearance-based methods

Step 2: Feature extraction  Holistic Feature Extraction  Eigenface  principal component analysis (PCA)  Local Feature Extraction  Gabor Wavelet Transform

Eigenface and PCA

Example

Step 3: Face recognition/verification  Verification  Recognition  Match  Database:The Yale Face Database

Applications  Entertainment: Smart camera/ Virtual Reality  Smart Cards: Passports  Security : Personal Device Logon /Real time screening  Law Enforcement: Anti-crime

Advantages  Non-intrusive biometric identification technique  Convenient and economic  Real time screening

Disadvantages  Strict requirement of light and pose  Face may change with time  Privacy issue

Conclusion  A popular research area in machine leaning with a broad application foreground.

Reference  M. Turk, A. Pentland, Eigenfaces for Recognition, Journal of Cognitive Neurosicence, Vol. 3, No. 1, 1991, pp  A. Pentland, B. Moghaddam, T. Starner, View-Based and Modular Eigenspaces for Face Recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 1994, Seattle, Washington, USA, pp  H. Moon, P.J. Phillips, Computational and Performance aspects of PCA-based Face Recognition Algorithms, Perception, Vol. 30, 2001, pp

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