Authors:
M. F. Karaaba
;
O. Surinta
;
L. R. B. Schomaker
and
M. A. Wiering
Affiliation:
University of Groningen, Netherlands
Keyword(s):
Eye-pair Detection, Eye Detection, Face Alignment, Face Recognition, Support Vector Machine.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
In face recognition, face rotation alignment is an important part of the recognition process. In this paper, we
present a hierarchical detector system using eye and eye-pair detectors combined with a geometrical method
for calculating the in-plane angle of a face image. Two feature extraction methods, the restricted Boltzmann
machine and the histogram of oriented gradients, are compared to extract feature vectors from a sliding window.
Then a support vector machine is used to accurately localize the eyes. After the eye coordinates are
obtained through our eye detector, the in-plane angle is estimated by calculating the arc-tangent of horizontal
and vertical parts of the distance between left and right eye center points. By using this calculated in-plane
angle, the face is subsequently rotationally aligned. We tested our approach on three different face datasets:
IMM, Labeled Faces in the Wild (LFW) and FERET. Moreover, to compare the effect of rotational aligning on
face recognition
performance, we performed experiments using a face recognition method using rotationally
aligned and non-aligned face images from the IMM dataset. The results show that our method calculates the
in-plane rotation angle with high precision and this leads to a significant gain in face recognition performance.
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