3 Face recognition
3.1 Face recognition methods

Face recognition systems fall into two categories: verification and identification.

Face verification is a 1:1 match. In this process face image whose identity is being claimed is compared to a template face images.

On the contrary, face identification is a 1:N problem. Face image is compared to all image templates in a face database to determine the identity of the query face.

In case we do not know if the tested face is in the system database, process is as following. The query face image is also compared against all the face images in the database, evaluating probability for each one. All these probabilities are numerically ranked: the highest value is first. In case probability is higher than given threshold, system notifies us about the result [1].

The selected basic 2D face recognition methods:

- Linear/nonlinear projection methods
  - Principal Component Analysis (PCA) - the method based on PCA is named eigenface. The major idea of PCA is to decompose a data space into a linear combination of a small collection of bases, which are pair wise orthogonal and which capture the directions of maximum variance in the training set [4].
  - Kernel Principal Component Analysis (KPCA) - is a method of non-linear feature extraction. The KPCA can extract the feature set which is more suitable in categorization than the conventional PCA. The KPCA has been widely used in the case of face recognition with face expression and under varying illumination [4].
  - Linear Discriminant Analysis (LDA) - has been proposed as a better alternative to the PCA. It provides discrimination among the classes, while the PCA deals with the input data in their entirety, without paying any attention for the underlying structure. Indeed the main aim of the LDA consists in finding a base of vectors providing the best discrimination among the classes, trying to maximize the between-class differences, minimizing the within-class ones [1].
  - Discriminant Common Vectors (DCV) - the main idea of the DCV consists in collecting the similarities among the elements in the same class dropping their dissimilarities [1].
- The neural networks – is nonlinear solution, is used also in other pattern recognition problems. The advantage of neural classifiers over linear ones is that they can reduce misclassifications among the neighborhood classes. The basic idea is to consider a net with a neuron for every pixel in the image. Nevertheless, because of the pattern dimensions neural networks are not directly trained with the input images, but the application of a dimensionality reduction technique is used before training [1].
- Fractal and iterated function systems (IFS) – IFS theory has been mainly developed in the area of image coding and lately has been extended to the image indexing. Fractal code of an image is invariant with respect to a wide set of global transformations, such as rotations, contrast scaling, etc. IFS fractal of a face image is used for training a neural networks, where is used as clasifier [1].