A generative adversarial mechanism and attention mechanism-based standard face generation method, comprising: a dataset design step, constructing, according to database-related annotation data, face code having a plurality of non-limiting factors for a face image, and taking the code and the face image as inputs of a model; a model design and training step, using a generative adversarial mechanism and an attention mechanism to design a corresponding network structure, and using the constructed data pair to perform model training, so as to obtain a network model weight; and a model prediction step, predicting the acquired face image by means of the model. The present invention applies deep learning network technology to standard face generation to generate a colour, front-facing, and standard face image under normal light illumination. The method using a deep learning network is capable of obtaining an accurate standard face photograph, reducing the difficulty of matching with data in a single-sample database, and laying a solid foundation for subsequent face feature extraction and single-sample facial recognition.
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