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Zhang, Xu, Zhang, Shujun 
ORCID: 0000-0001-5699-2676 and Hapeshi, Kevin
(2010)
A new method for face detection in colour images for emotional bio-robots.
Science China Technological Sciences, 53 (11).
pp. 2983-2988.
doi:10.1007/s11431-010-4132-z
Abstract
Emotional bio-robots have become a hot research topic in last two decades. Though there have been some progress in research, design and development of various emotional bio-robots, few of them can be used in practical applications. The study of emotional bio-robots demands multi-disciplinary co-operation. It involves computer science, artificial intelligence, 3D computation, engineering system modelling, analysis and simulation, bionics engineering, automatic control, image processing and pattern recognition etc. Among them, face detection belongs to image processing and pattern recognition. An emotional robot must have the ability to recognize various objects, particularly, it is very important for a bio-robot to be able to recognize human faces from an image. In this paper, a face detection method is proposed for identifying any human faces in colour images using human skin model and eye detection method. Firstly, this method can be used to detect skin regions from the input colour image after normalizing its luminance. Then, all face candidates are identified using an eye detection method. Comparing with existing algorithms, this method only relies on the colour and geometrical data of human face rather than using training datasets. From experimental results, it is shown that this method is effective and fast and it can be applied to the development of an emotional bio-robot with further improvements of its speed and accuracy.
| Item Type: | Article |
|---|---|
| Article Type: | Article |
| Uncontrolled Keywords: | face detection skin colour model eye detection bio-robots |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Schools and Research Institutes > School of Business, Computing and Social Sciences |
| Research Priority Areas: | Applied Business & Technology |
| Depositing User: | Anne Pengelly |
| Date Deposited: | 28 Apr 2015 15:00 |
| Last Modified: | 31 Aug 2023 08:01 |
| URI: | https://eprints.glos.ac.uk/id/eprint/2141 |
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