A method for detecting an occluded image occluded by a finger that blocks an edge of a lens of a camera, comprising: after an image is captured by a camera, obtaining the image as an image to be detected; inputting the image to be detected into a trained occluded-image detection model, wherein, the occluded-image detection model is trained based on original occluded images and non-occluded images by using a trained data feature augmentation network; determining whether the image to be detected is an occluded image based on the occluded-image detection model; and outputting an image detection result; characterized by the method further comprising: obtaining the original occluded images and the non-occluded images; generating a finger template image and a non-finger image based on each original occluded image, wherein the finger template image is obtained by removing an area not occluded by the finger from the original occluded image, and the non-finger image is obtained by removing an area occluded by the finger from the original occluded image; generating a plurality of training data sets, wherein each training data set comprises one finger template image, one non-finger image and one original occluded image, and a plurality of training data sets are used to train the data feature augmentation network in each round of training; and training the occluded-image detection model based on the original occluded images and the non-occluded images by using an occlusion image feature synthesized by the trained data feature augmentation network after an image is captured by a camera, obtaining the image as an image to be detected; inputting the image to be detected into a trained occluded-image detection model, wherein, the occluded-image detection model is trained based on original occluded images and non-occluded images by using a trained data feature augmentation network; determining whether the image to be detected is an occluded image based on the occluded-image detection model; and outputting an image detection result; characterized by the method further comprising: obtaining the original occluded images and the non-occluded images; generating a finger template image and a non-finger image based on each original occluded image, wherein the finger template image is obtained by removing an area not occluded by the finger from the original occluded image, and the non-finger image is obtained by removing an area occluded by the finger from the original occluded image; generating a plurality of training data sets, wherein each training data set comprises one finger template image, one non-finger image and one original occluded image, and a plurality of training data sets are used to train the data feature augmentation network in each round of training; and training the occluded-image detection model based on the original occluded images and the non-occluded images by using an occlusion image feature synthesized by the trained data feature augmentation network obtaining the original occluded images and the non-occluded images; generating a finger template image and a non-finger image based on each original occluded image, wherein the finger template image is obtained by removing an area not occluded by the finger from the original occluded image, and the non-finger image is obtained by removing an area occluded by the finger from the original occluded image; generating a plurality of training data sets, wherein each training data set comprises one finger template image, one non-finger image and one original occluded image, and a plurality of training data sets are used to train the data feature augmentation network in each round of training; and training the occluded-image detection model based on the original occluded images and the non-occluded images by using an occlusion image feature synthesized by the trained data feature augmentation network <EOS>