Authors:
Ayesha Hakim
;
Stephen Marsland
and
Hans W. Guesgen
Affiliation:
Massey University, New Zealand
Keyword(s):
Face Detection, Haar-classifier, Skin-colour, Occlusion.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
Statistical Approach
Abstract:
The progress of computer vision technology has opened new doors for interactive and friendly computer interfaces. Human face detection is an essential step of various human-related computer applications, including face recognition, emotion recognition, lip reading, and several intelligent human computer interfaces. Since it is the basic step in such applications, it must be reliable enough to support further steps. Several approaches to detecting human faces have been proposed so far, but none of them can detect faces in all different conditions such as varying lighting conditions; frontal, profile, tilted and rotated faces; occlusions by glasses, hijab, facial hair; and noise. We propose a more reliable hybrid approach that is able to detect human faces in multiple circumstances. Moreover, a brief, but comprehensive, review of the literature is presented that may be useful to evaluate any face detection system. Our proposed approach gives up to 97% accuracy on 600 images (both simpl
e and complicated), which is the highest accuracy rate reported to date to our knowledge.
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