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.

分析金融市场时，它需要从海量数据中挖掘有效信息。但是，难以从图像信息获得信息。为了提高金融市场分析的效率，将虹膜识别算法应用于金融图像数据分析中，提出了一种基于形态骨架和Gabor滤波器的特征提取与识别算法。该算法使用多频，多方向二维Gabor滤波器提取局部特征，并将提取的特征码与虹膜识别方法结合起来，以完成对类别内虹膜和类别间虹膜的识别。另外，为了验证该算法的有效性，本研究以MatalabGUI为平台构建实验模型。总而言之，在这项研究中，金融图像用作研究图像以进行识别和分析。研究结果表明，本文提出的算法具有一定的效果。