Biometric Recognition Methodology part 4/4 - Normalization

3.6 Normalization


Since the value of Backpropagation neural network required input range from zero (0) to one (1) as used the sigmoid activation function and it is found that most of the result produced in feature extraction using eigenfaces are not in particular range, thus the normalization process are required. In these proposed neural network, three types of different normalizations techniques had been selected for this reason [Puteh Saad, 2001]. They are the simple unit range (SUR), improve unit range (IUR) and improved linear scaling (ILS). Equation (3.42), (3.43) and (3.44) shows the computation needed for SUR, IUR and ILS respectively. The best technique adopted is based on the highest classification rate produced by backpropagation neural network.

With reference to the above equation  refers to the new value of feature,  in each dimension after the normalization process. Furthermore xmax and xmin refer to the maximum and minimum features value respectively. For the ILS computation,  refers to the mean of the feature and  is used for the standard deviation of the features in the same dimension. The dimensions for each vector are defined as .



3.7 Cross Validation Setup


In order to examine the generalization performance of backpropagation neural network, the cross validation techniques is used. Thus, for this purpose each of the databases had to be divided randomly into 3 groups. First and second groups are the same person or class but only first group is used in the training phase and second group contain unknown face but still the same person as in the first class. The third group is the unknown person and surely different class from first and second group. Second and third class is used as the testing pattern.


The research hypothesis, the second group should give high recognition percentage as the same person but difference variation of lighting, face emotion or pose. However the recognition rate should be low for the third group because it was difference person and the proposed model should classify as unknown.




3.8 Summary


In this chapter the methodology of proposed system is described to achieve the main objectives to recognize an unknown face image. The mathematical aspects of the Principal Component Analysis (PCA) including the numerical Jacobi’s Method to perform eigenvalues, eigenvectors and neural network are combined with easy explanation. The pseudo code for each methods is also showed for simple implementation and understanding. Three different models have been proposed and explained in this chapter. Figure 3.19 shows the experiments model design conducted in the research. The result and discussion will be discussed in the next chapter.

Figure 3.19: Proposed System Design