A method for detecting a finger occlusion image that is an image with occlusion by fingers, characterized by comprising: acquiring N first original finger occlusion images and M first finger non-occlusion images, where N and M are positive integers and N<M, and generating a first training data set based on the first original finger occlusion images and the first finger non-occlusion images ; performing, based on the first training data set, first training on a neural network model for detection of the finger occlusion image ; acquiring L second original finger occlusion images and K second finger non-occlusion images, where L and K are positive integers and L<K, and generating a second training data set based on the second original finger occlusion images and the second finger non-occlusion images , wherein a number of types of image scenes comprised in the K second finger non-occlusion images selected from all-scene non-occlusion images is less than a number of types of image scenes comprised in the M first finger non-occlusion images selected from all-scene non-occlusion images, and the K second finger non-occlusion images comprise error-prone scenes, wherein the error-prone scenes comprise beach or sky with burning clouds, which are particularly easy to be mistaken in finger occlusion detection; replacing a linear classifier that is a last layer of the neural network model that has completed the first training with an iterative training module to form a finger occlusion image detection model , the iterative training module being trained alternately based on image scene features and image occlusion features comprised in the second training data set so as to implement model training; performing second training on the finger occlusion image detection model based on the second training data set ; and inputting an image to be detected into the finger occlusion image detection model that has been trained, to determine whether the image to be detected is the finger occlusion image . acquiring N first original finger occlusion images and M first finger non-occlusion images, where N and M are positive integers and N<M, and generating a first training data set based on the first original finger occlusion images and the first finger non-occlusion images ; performing, based on the first training data set, first training on a neural network model for detection of the finger occlusion image ; acquiring L second original finger occlusion images and K second finger non-occlusion images, where L and K are positive integers and L<K, and generating a second training data set based on the second original finger occlusion images and the second finger non-occlusion images , wherein a number of types of image scenes comprised in the K second finger non-occlusion images selected from all-scene non-occlusion images is less than a number of types of image scenes comprised in the M first finger non-occlusion images selected from all-scene non-occlusion images, and the K second finger non-occlusion images comprise error-prone scenes, wherein the error-prone scenes comprise beach or sky with burning clouds, which are particularly easy to be mistaken in finger occlusion detection; replacing a linear classifier that is a last layer of the neural network model that has completed the first training with an iterative training module to form a finger occlusion image detection model , the iterative training module being trained alternately based on image scene features and image occlusion features comprised in the second training data set so as to implement model training; performing second training on the finger occlusion image detection model based on the second training data set ; and inputting an image to be detected into the finger occlusion image detection model that has been trained, to determine whether the image to be detected is the finger occlusion image . <EOS>