Lately, human recognition and identification has acquired much more attention than it had before, due to the fact that computer science nowadays is offering lots of alternatives to solve this problem, aiming to achieve the best security levels. One way is to fuse different modalities as face, voice, fingerprint and other biometric identifiers. The topics of computer vision and machine learning have recently become the state-of-the-art techniques when it comes to solving problems that involve huge amounts of data. One emerging concept is Artificial Neural networks. In this work, we have used both human face and voice to design a Multibiometric recognition system, the fusion is done at the feature level with three different schemes namely, concatenation of pre-normalized features, merging normalized features and multiplication of features extracted from faces and voices. The classification is performed by the means of an Artificial Neural Network. The system performances are to be assessed and compared with the Knearest-neighbor classifier as well as recent studies done on the subject. An analysis of the results is carried out on the basis Recognition Rates and Equal Error Rates. Povzetek: Z nevronsko mrežo so kombinirani obraz in glas za biometrično identifikacijo.

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In this work, an objective comparison between some common global appearance face recognition based methods (PCA, FLD, SVD, DCT, DWT and WPD) has been carried out when considering some natural effects that may decrease the performances. In particular, effects such as blur, motion, noise and their combination are taken into account. To evaluate the performances, FEI database containing images corresponding to 200 individuals are used.For each individual, 14 positions have been considered. The quality of face reconnaissance is measured using the wellknown Equal Error Rate (EER) criteria. Interesting results are obtained highlighting the superiority, in some specific contexts, of some of the evaluated methods.

This study is a comparison between two image segmentation's methods; the first method is based on normal brain's tissue recognition then tumor extraction using thresholding method. The second method is classification based on EM segmentation which is used for both brain recognition and tumor extraction. The goal of these methods is to detect, segment, extract, classify and measure properties of the brain normal and abnormal (tumor) tissues

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