원문정보
초록
영어
Face recognition from the real data, capture images, sensor images and database images is challenging problem due to the wide variation of face appearances, illumination effect and the complexity of the image background. Face recognition is one of the most effective and relevant applications of image processing and biometric systems. In this paper we are discussing the face recognition methods, algorithms proposed by many researchers using artificial neural networks (ANN) which have been used in the field of image processing and pattern recognition. How ANN will used for the face recognition system and how it is effective than another methods will also discuss in this paper. There are many ANN proposed methods which give overview face recognition using ANN. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included, and also the performance analysis of different ANN approach and algorithm is analysing in this research study.
목차
1. Introduction
2. Structure of Face Recognition System
2.1. Face Detection
2.2. Pre-processing
2.3. Feature Extraction
2.4. Face Recognition
3. Neural Network
3.1. Topologies of Neural Network
3.2 Types of Artificial Neural Networks
4. Artificial Neural Networks Approaches for Face Detection
4.1. PCA with Artificial Neural Networks
4.2. Deep Convolution Neural Networks
4.3. Radial Basis Function Neural Networks
4.4. Convolutional Neural Network Cascade
4.5. Bilinear CNNs
4.6. Back Propagation Network (BPN) and Radial Basis Function Network (RBF)
4.7. Retinal Connected Neural Network (RCNN)
4.8 Rotation Invariant Neural Network (RINN)
4.9 Fast Neural Network
4.10 Evolutionary Optimization of Neural Networks
4.11 Multilayer Perceptron (MLP)
4.12 Gabor Wavelet Faces with ANN
4.13 Hybrid Wavelet Neural Network and Switching Particle Swarm Optimization Algorithm
5. Conclusion
6. Future Work
References