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Automated Facial Recognition:. Overview and Applications by Brandon Hume. Human vs. Computer Vision. Computer vision still has a long way to go to match the capabilities of human face recognition. People use non-face features to aid in recognition.
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Automated Facial Recognition: Overview and Applications by Brandon Hume
Human vs. Computer Vision • Computer vision still has a long way to go to match the capabilities of human face recognition. • People use non-face features to aid in recognition. • Human recognition is biased, and has memory limitations.
Applications • Information Security • Law enforcement • Social / Entertainment • Security and Surveillance
Recognition Steps • Detection and rough normalization of faces • Feature extraction and accurate normalization of faces • Identification and/or verification.
General Pattern Matching Methods • Holistic - use the whole face region as the raw input to a recognition system • Feature-based - use individual features to verify matches • Hybrid - incorporate aspects of both holistic and feature-based recognition.
Algorithms • Principal Component Analysis (PCA) • Linear Discriminant Analysis (LDA) • Iterative Closest Point (ICP) • Eigenfaces - Uses PCA analysis to derive a set of "standardized face ingredients", from statistical analysis of a database of face images
Eigenface images From AT&T Laboratories Cambridge
Lighting / illumination Expression Age Blur Distance Rotation Obstructions (hair, clothing, and glasses) Complications
Testing • Probability of Detection (Pd) • False Alarm Rate • Missed Alarm Rate
Image Databases • FERET - 14,126 total images • FRVT - Face Recognition Vendor Test 121,589 images
Ethics • Security vs. Privacy • Recent News – Google, Recognizr
Open Source • Colorado State University’s - http://www.cs.colostate.edu/evalfacerec/ • OpenCV - http://code.google.com/p/opencvdotnet/ • EmguCV - http://sourceforge.net/projects/emgucv/files/