Abstract
When analyzing financial markets, it needs to mine effective information from massive data. However, it is difficult to obtain information from image information. In order to improve the efficiency of financial market analysis, this paper applies the iris recognition algorithm to financial image data analysis and proposes a feature extraction and recognition algorithm based on morphological skeleton and Gabor filter. The algorithm uses a multi-frequency, multi-directional 2D Gabor filter to extract local features and combines the extracted feature codes with the iris recognition method to complete the identification of intra-class irises and inter-class irises. In addition, in order to verify the effect of the algorithm, this study uses MatalabGUI as a platform to build an experimental model. In summary, in this study, financial images are used as research images for identification and analysis. The research results show that the algorithm proposed in this paper has a certain effect.
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