Real Time Tracking and Face Recognition Using Web Camera
Much interest has been shown in the field of biometric surveillance over the past decade. Face Recognition is a biometric recognition system that has gained much attention due to its low intrusiveness and easy availability of input data. To humans, face recognition is a natural ability that is an...
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| 格式: | Thesis |
| 语言: | 英语 英语 |
| 出版: |
2005
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| 主题: | |
| 在线阅读: | http://psasir.upm.edu.my/id/eprint/6030/1/FK_2005_23.pdf |
| 总结: | Much interest has been shown in the field of biometric surveillance over the past decade.
Face Recognition is a biometric recognition system that has gained much attention due to
its low intrusiveness and easy availability of input data. To humans, face recognition is a
natural ability that is an easy task. However, computerized face recognition is often
complex and inaccurate. Several good techniques such as template matching, graph
matching and eigenfaces have been developed by researchers to accomplish this task to
varying degrees of success.
In this dissertation, the eigenface approach is combined with neural networks to perform
face recognition. Face images are first projected into a feature space where eigenvectors
are extracted. The neural network performs identification and is used to train the
computer to recognize faces.
A number of very good approaches to face recognition are already available. Most of
them work well in constrained environments. Here the development of a real time face recognition system that should work well in an unconstrained environment is studied. A
tracking system is developed to work together with the face recognition algorithm. A
method using pixel difference is used to detect movements in the camera's view. A pantilt
system, using stepper motors is used to enable horizontal and vertical movements.
The face recognition algorithm is found to be working well with a recognition rate of
around 95%. Eigenface method combined with neural networks displays good
performance in terms of accuracy and the ability for learning and generalization. The
tracking system works well for objects traveling speeds below 5mIs and at distances from
between 0.5m to 2m from the camera. Several improvements are suggested to improve
the tracking system performance. An overview of some leading tracking and face
recognition systems and scope of future work in this area is discussed. |
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