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As a new manner of biometrics measurement, human finger vein pattern has been developed. Many researchers have paid close attention to this topic. In this paper, three methodologies of features extraction are used for finger vein verification system. These methods are; Grey Level Co-occurrence Matrix (GLCM), Tamura, and Scale Invariant Feature Transform (SIFT). Empirically, the results of the proposed algorithm was acceptable and better.

Image processing and computer vision have a major role in addressing many problems, where images and techniques that are dealt with them contribute greatly to finding solutions to many topics and in different directions. Classification techniques have a large and important role in this field, through which it is possible to recognize and classify images in a way that helps in solving a specific problem. Among the most prominent models that are distinguished for their ability and accuracy in distinguishing is the CNN model. In this research, we have introduced a system to classify the sea coral images because sea coral and its classes have many benefits in many aspects of our lives. The important thing in this work is to study four CNN architectures model (i.e., AlexNet, SqueezeNet, to determine the accuracy and efficiency of these architectures and determine the best of them with coral image data, and we are shown the details in the research paragraphs. The results showed 83.33% accuracy for AlexNet, 80.85% SqueezeNet, 90.5% GoogLeNet and 93.17% for Inception-v3.Povzetek: Predstavljena je uporaba arhitektur konvolucijskih nevronskih mrež (CNN) za razvrščanje slik morskih koral.

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