SOFTWARE FOR COMPUTER VISION. FACE RECOGNITION AND SIMILAR

SOFTWARE FOR COMPUTER VISION. FACE RECOGNITION AND SIMILAR.

1. Introduction to Computer Vision. In PDF format. Computer Vision System. Image Enhancement. Image analysis. Pattern Classification.
2. Overview of face recognition and its applications. Slides in PPT. Face detection using color information. Face matching. Face Segmentation/Detection. Facial feature extraction. Face recognition. Video-based face recognition. Comparison of methods. Literature.
3. Associative memories. File in PPT. Associative memories. Capacity versus Robustness challenge in Associative Memories. Morphological Memories. Improving limitations. Results. References.
4. Application of boosting in Face recognition. Slides in PPT. Robust Real Time Face Detection. AdaBoost - learning algorithm. Face detection in real life. Using AdaBoost for face detection. Improvements. Demonstration.
5. Neural Nets and Connectionism. Slides in PPT. Types of Neural Nets. Connectionism. Learning versus unsupervised learning. Network Architectures. Single Layer Feed-Forward. Multi Layer Feed-Forward. Recurrent Network. The neuron. Bias as extra input. Dimensions of a neural network. Face recognition. Handwritten digit recognition. The XOR problem. Hidden Units. Backpropagation nets. Training a backpropagation net. The feedforward stage. Backpropagation. Adjusting the weights. The learning rate. Example: one layer. Multi-layer and training.
6. Neural Networks. Slides in PPT. What are Neural Networks? Biological Neural Networks. ANN - The basics. Feed Forward Networks. Training. Voice Recognition using NN. Applications of Feed-Forward Networks. Recurrency. Elman Nets. Hopfield Nets. Central Pattern Generation.
7. Neural Nets. Slides in PPT. Perceptrons. Learning. Hidden Layer Representations. Speed Up Training. Bias, Overfitting and Early Stopping. Example - face recognition.
8. Eigenfaces and Neural Nets. SOM. Slides in PPT. Content Based Face Recognition. Difference from Image Recognition. Approach. Stages of face recognition. Face Recognition using Eigen Faces. Steps in Face Recognition. PCA. Eigenfaces. Classification using Nearest Neighbor. Neural Networks and TS-SOM. What is SOM? Training of SOM. Algorithm. Relevance Feedback. Interaction between User and System. Comparison of the two approaches. Future work. References.
9. Face detection using SVM. Slides in PPT. Label the group photo. Project description. Face detection. Possible Solution. Support Vector Machines algorithm. Application to face detection. Implementation. Results. Examples. Future plans. References.
10. Face Recognition using symmetry and other features. Slides in PPT. Localization of the lips. Basic idea. Golden Section. Symmetrical features in face. In rotation. Symmetry of eyes and face. Measuring of symmetry. Examples.
11. Preprocessing. Slides in PPT. Preprocessing for face recognition. Highlights for the approach. Recognition principle. Inverse Estimation. Measure of Bijectivity. Mapping properties. Preprocessing example. Performance. Preprocessing for illumination correction. Comparison with existing methods. Preprocessing and recognition examples.