# Design and Implementation of a Multi-Modal Biometric System for Company Access Control +
## Materials and Methods
An interesting approach which can be used for face recognition is the Pyramid Match Kernel [17], which is based on a fast kernel function which maps unordered feature sets to multi-resolution histograms and computes a weighted histogram intersection in this space. Other methods for implementing face recognition are: the eigenface method using principal component analysis (PCA), which is based on the idea that a high-dimensional dataset is often described by correlated variables and therefore only a few meaningful dimensions account for most of the information; the PCA method finds the directions with the greatest variance in the data, called principal components. The Fisherfaces method uses linear discriminant analysis (LDA), which performs a class-specific dimensionality reduction; in order to find the combination of features that separates best between classes, it maximizes the ratio of between-classes to within-classes scatter [18]. The chosen methodology is the Local Binary Patterns (LBP). It is based on the extraction of local features from images; the basic idea is to summarize the local structure in an image by comparing each pixel with its neighbourhood. Then, the LBP image is divided into local regions and a histogram is extracted from each one. Finally, the spatially-enhanced feature vector (called Local Binary Patterns Histograms (LBPH) [18]) is obtained by concatenating all the local histograms. The application of facial recognition is expected to happen in a dynamic but somehow controlled environment in which a person who wants to be authenticated has to walk along a hallway following an ordered queue so that the sensor can easily detect whether a face is present in the acquired image and locate it. [PERSON]'s method has been chosen for iris recognition. It consists of several phases: segmentation (i.e., the location of the iris in the eye image), normalization with the Daugman's Rubber Sheet Model, encoding with the Log-Gabor wavelet and matching using the Hamming Distance [19]. A previous work involving face and iris for identity verification is described in [20]. These two biometric traits are combined using two different fusion strategies: the former is based on the computation of either an unweighted or weighted sum and the comparison of the result to a threshold, the latter considers the matching distances of face and iris classifiers as a two-dimensional feature vector and it uses a classifier, such as [PERSON]'s discriminant analysis and a neural network with radial basis function (RBFNN), to classify the vector as being genuine or an impostor. A convenient mode in which a multi-modal system can operate is the serial mode: it means that the two biometric characteristics do not have to be acquired simultaneously and that a decision could be arrived at without acquiring all the traits. This last aspect is very important, especially for those applications where there are time constraints, because it leads to a reduction of the overall recognition time. The proposed multi-modal biometric system relies on two different modules: the module for face identification and the module for iris verification. The fusion methodology adopted at the decision level is a post-classification method, and it follows the OR rule; i.e., it is sufficient that only a biometric trait is recognized as genuine to lead to a positive final decision. This serial matching approach gives the possibility of not acquiring all the traits; for example, only face recognition is considered if the information collected at the first module is believed to be enough to determine if a user is genuine or an impostor. The system consists of several different submodules, each of them providing its own functionality. There are two _sensor modules_ for face and iris acquisition, which capture the raw biometric data. In the _feature extraction modules_, the acquired data is processed to extract a set of salient and discriminatory features.