Authors:
Jacek Naruniec
1
;
Władysław Skarbek
1
and
Antonio Rama
2
Affiliations:
1
Faculty of Electronics and Information Technology, Warsaw University of Technology, Poland
;
2
Universitat Politécnica de Catalunya (UPC), Spain
Keyword(s):
Face detection, face tracking, Gabor filter, Linear Discriminant Analysis, Dual Linear Discriminant Analysis, reference graph.
Related
Ontology
Subjects/Areas/Topics:
Biometrics and Pattern Recognition
;
Image and Video Processing, Compression and Segmentation
;
Multidimensional Signal Processing
;
Multimedia
;
Multimedia Signal Processing
;
Telecommunications
Abstract:
The paper presents a novel face detection and tracking algorithm which could be part of human-machine interaction in applications such as intelligent cash machine. The facial feature extraction algorithm is based
on discrete approximation of Gabor Transform, called Discrete Gabor Jets (DGJ), evaluated in edge points. DGJ is computed using integral image for fast summations in arbitrary windows and by FFT operations on short contrast signals. Contrasting is performed along radial directions while frequency analysis along angular directions. Fourier coefficients for a small number of rings create a feature vector which is next reduced to few LDA components and then compared to the reference facial feature vector. Detected eyes and nose corners are chosen to fit reference face by spatial relationships. Tracking is based on the same rule, but the corners are searched only within already detected facial features neighborhood. Optionally for face normalization eyes centers are found as ce
nters of outer and inner eye corners. Comparison of manual and automatic eye center detection shows still significant advantage of manual approach, measured in terms of accuracy in face recognition by Linear Discriminant Analysis (LDA) and Dual Linear Discriminant Analysis (DLDA) algorithms.
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