원문정보
초록
영어
Face recognition is the process of identifying one or more people in images or videos. It is an important part of biometric, security & surveillance system, and image indexing systems. Various face recognition techniques have been proposed in literature such as: Eigen-faces, Feature based, Hidden Markov model and Neural network based techniques. The first three techniques mostly include a phase of feature extraction or preprocessing closely related to the type of image to recognize. On the other hand Neural network technique does not need specific data about the type of image, thus can be applied to any type of image and at the same time provides better accuracy. In this paper we made an effort to combine neural network technique with fuzzy logic. Our experimental result shows that combining the two provide better accuracy in comparison to other techniques mentioned above.
목차
1. Introduction
2. Face Recognition Techniques
2.1. Eigenfaces
2.2. Feature-based
2.3. Hidden Markov Model
2.4. Neural Techniques
3. Proposal
3.1. Training of the Neural Network:
3.2. Recognition of the data using Fuzzy Logic:
4. Simulation Set up Parameters
4.1. Designed Architecture
4.2. Performance Metrics
4.3. Setup Parameters
5. Simulation Results
5.1. Impact on Accuracy
5.2. Impact on Error Rate
6. Conclusion
References