Miscellaneous
Following is a list major Face Recognition Algorithms.
Ada-Boosted Gabor
Gabor features have been recognized as one of the most successful face representations, but it is too high dimensional for fast extraction and accurate classification. AdaBoost is exploited to select optimally the most informative Gabor features.
Neural Network
A neural network learning algorithm called Backpropagation is among the most effective approaches to machine learning when the data includes complex sensory input such as face images. The network is being trained on the pictures from the database first, and then it is ready to identify face pictures given to it.
- Neural Networks for Face Recognition
- Face Recognition using Neural Network
- Neural Networks: FAQ, Papers, and Various Other Things
Principal Component Analysis (PCA)
Derived from Karhunen-Loeve's transformation. Given an s-dimensional vector representation of each face in a training set of images, Principal Component Analysis (PCA) tends to find a t-dimensional subspace whose basis vectors correspond to the maximum variance direction in the original image space. This new subspace is normally lower dimensional (t << s). If the image elements are considered as random variables, the PCA basis vectors are defined as eigenvectors of the scatter matrix.
Support Vector Machine (SVM)
Given a set of points belonging to two classes, a Support Vector Machine (SVM) finds the hyperplane that separates the largest possible fraction of points of the same class on the same side, while maximizing the distance from either class to the hyperplane. PCA is first used to extract features of face images and then discrimination functions between each pair of images are learned by SVMs.
