Abstract: The method includes: receiving an image to be detected, wherein the image to be detected includes an object to be detected; detecting the image to be detected based on a trained multi-task network model to obtain a region where the object wears a mask; comparing an area of the region where the object wears the mask to a predetermined threshold to determine a first detection result; in response to the object being determined as wearing the mask, determining comparison information of a mask area to be compared, and determining a target region in the image to be detected corresponding to the comparison information based on a face feature point template; and obtaining a second detection result based on an overlapping degree between the target region and the region where the object wears the mask.

Abstract: An identity recognition method, a computer apparatus, a non-transitory computer-readable storage medium are provided.

Abstract: Systems and methods for face recognition are provided. The systems may perform the methods to obtain a neural network comprising a first sub-neural network and a second sub-neural network; generate a plurality of preliminary feature vectors based on an image associated with a human face, the plurality of preliminary feature vectors comprising a color-based feature vector; obtain at least one input feature vector based on the plurality of preliminary feature vectors; generate a deep feature vector based on the at least one input feature vector using the first sub-neural network; and recognize the human face based on the deep feature vector.

Abstract: A method may include obtaining an image including a face. The method may further include determining at least one time domain feature related to the face in the image and at least one frequency domain information related to the face in the image. The method may further include evaluating the quality of the image based on the at least one time domain feature and the frequency domain information.

Abstract: A neural network-based image processing method may include receiving, by a trained neural network, a first image including a first object, the first object being partially covered by a second object. The method may also include generating, by the trained neural network, a second image based on the first image. The second image is a representation of the first image with the second object substantially removed, and the first object is a human face.

Abstract: A method may include obtaining an image including a face. The method may further include determining at least one time domain feature related to the face in the image and at least one frequency domain information related to the face in the image. The method may further include evaluating the quality of the image based on the at least one time domain feature and the frequency domain information.

Abstract: A method may include obtaining an image including a face. The method may further include determining at least one time domain feature related to the face in the image and at least one frequency domain information related to the face in the image. The method may further include evaluating the quality of the image based on the at least one time domain feature and the frequency domain information.

Abstract: Systems and methods for face recognition are provided. The systems may perform the methods to obtain a neural network comprising a first sub-neural network and a second sub-neural network; generate a plurality of preliminary feature vectors based on an image associated with a human face, the plurality of preliminary feature vectors comprising a color-based feature vector; obtain at least one input feature vector based on the plurality of preliminary feature vectors; generate a deep feature vector based on the at least one input feature vector using the first sub-neural network; and recognize the human face based on the deep feature vector.

Abstract: A neural network-based image processing method may include receiving, by a trained neural network, a first image including a first object, the first object being partially covered by a second object. The method may also include generating, by the trained neural network, a second image based on the first image. The second image is a representation of the first image with the second object substantially removed, and the first object is a human face.

Abstract: A method may include obtaining an image including a face. The method may further include determining at least one time domain feature related to the face in the image and at least one frequency domain information related to the face in the image. The method may further include evaluating the quality of the image based on the at least one time domain feature and the frequency domain information.