Groups of starlings can form impressive shapes as they travel northward together in the springtime. This is among a group
of natural phenomena based on swarm behaviour. The research field of artificial intelligence in computer science,
particularly the areas of robotics and image processing, has in recent decades given increasing attention to the underlying
structures. The behaviour of these intelligent swarms has opened new approaches for face detection as well. G. Beni and J.
Wang coined the term “swarm intelligence” to describe this type of group behaviour. In this context, intelligence describes
the ability to solve complex problems.
The objective of this project is to automatically find exactly one face on a photo or video material by means of swarm
intelligence. The process developed for this purpose consists of a combination of various known structures, which are then
adapted to the task of face detection. To illustrate the result, a 3D hat shape is placed on top of the face using an example
application program.:1 Introduction
1.1 Face Detection
1.2 Swarm Intelligence and Particle Swarm Optimisation Fundamentals
3 Face Detection by Means of Particle Swarm Optimisation
3.1 Swarms and Particles
3.2 Behaviour Patterns
3.2.1 Opportunism
3.2.2 Avoidance
3.2.3 Other Behaviour Patterns
3.3 Stop Criterion
3.4 Calculation of the Solution
3.5 Example Application
4 Summary and Outlook
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:19439 |
| Date | January 2010 |
| Creators | Lang, Andreas |
| Contributors | Ritter, Marc |
| Publisher | Technische Universität Chemnitz |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Source | 22nd European Union Contest for Young Scientists (EUCYS), Lissabon, September 2010. |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0031 seconds