4. Conclusion

Recognition of people in biometric systems is based on the physiological or behavioral features that a person possesses.

In this chapter, we presented the image preprocessing operations used in static biometric systems (physiological modality). In particular, we discussed operations related to the transformation of the brightness scale of the image, modification of the brightness histogram, median filtering, edge detection, and image segmentation. In the course of these operations, we obtain an image enabling the extraction and measurement of features that serve as the basis for recognition.

Next, we discuss the feature extraction process, focusing on certain geometrical features and texture features. We present a representation of texture features based on parameters obtained from the co-occurrence matrix and images after Gabor's filtration with various scaling and orientation parameters. In terms of geometric features, we discuss the moment-based features and geometrical features based on the topological properties of the image.

We provide and discuss the feature extraction process in the images of the blood vessels of the hand dorsal and wrist. We present features calculated on the basis of matrix of co-occurrences and texture characteristics obtained using Gabor's filter bank. The process of reducing the dimensionality of a feature vector using the PCA method is also considered.

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A Survey on Methods of Image Processing and Recognition for Personal Identification

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The main contributions of this chapter are the following:


The chapter can be the basis for further studies and works on the image processing in biometric systems, especially based on images of the blood vessel network.
