Image data enhancement strategy selection method and face recognition image data enhancement method

Abstract

The invention relates to an image data enhancement strategy selection method and a face recognition image data enhancement method. The image data enhancement strategy selection method comprises the steps of obtaining a data enhancement strategy search space and a target object image set; randomly selecting a plurality of data enhancement strategies in the search space; respectively calculating image classification errors corresponding to a plurality of randomly selected data enhancement strategies to obtain an initial data enhancement strategy error pair set; and searching and selecting an optimal data enhancement strategy from the search space by adopting a Bayesian optimization method. The face recognition image data enhancement method comprises the step of applying the selected optimaldata enhancement strategy to a to-be-enhanced face recognition image. According to the data enhancement strategy selection method, the optimal data enhancement strategy only needs to be selected onceaccording to the existing target object image set, the optimal data enhancement strategy can be applied to the same kind of target object image data for image data enhancement, and the use efficiencyof the image data enhancement method is improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for selecting an image data enhancement strategy and a face recognition image data enhancement method. Background technique [0002] With the rapid development of image processing technology, a large amount of labeled data is needed in many aspects, such as face recognition, vehicle recognition, etc., and obtaining sufficient data is a time-consuming and labor-intensive task. Manually labeling training data is a means of obtaining larger labeled datasets, however, it is a difficult and time-consuming way. Data augmentation is an effective alternative that leverages existing data to generate new data with label-preserving transformations. [0003] For example, in face recognition technology, a deep neural network model is usually used for recognition. The deep neural network model has a high recognition accuracy. However, in order to ensure the performance of t...

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Problems solved by technology

[0005] However, the strategies generated by these methods are usually not reusable. They are all based on a specific model or data set to discover the strategy, which will le

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