Abstract: The provided are a face pose estimation method and apparatus, an electronic device, and a storage medium. The method includes: obtaining a target image containing face information, and inputting the target image into a pre-constructed pose estimation model; performing feature extraction on the target image by using a shallow densely connected layer to obtain a plurality of first feature maps containing shallow feature information; performing an information fusion operation on the plurality of first feature maps by using a deep feature multiplexing layer to obtain a second feature map, such that deep feature information is fused into the shallow feature information; and extracting face pose information in the second feature map by using an attention layer to obtain a third feature map containing the face pose information, performing prediction on the third feature map by using a classifier, and determining a face pose in the target image.

Abstract: A face image clustering method and apparatus, an electronic device, and a storage medium are provided. The method includes: performing feature extraction on samples in a face data set by using a face recognition model to obtain a feature; calculating a cosine distance between each two features, and constructing a connected graph covering all the samples; on the basis of a connected component, searching the connected graph to obtain low-level subgraphs, and aggregating the low-level subgraphs, to obtain first candidate clusters; calculating quality scores and intersection scores, and screening the first candidate clusters to obtain second candidate clusters; outputting probability values of vertexes in the second candidate clusters by using a graph convolutional neural network, and removing noise points to obtain third candidate clusters; and searching for a shared vertex between each of the other third candidate clusters and a reference cluster, and removing the shared vertex.

Abstract: An expression recognition method and apparatus are provided. The method includes: acquiring a to-be-recognized image; obtaining an image feature map of the to-be-recognized image according to the to-be-recognized image; determining global feature information and local feature information according to the image feature map; and determining an expression type of the to-be-recognized image according to the global feature information and the local feature information. Since the global feature information of the to-be-recognized image reflects overall information of a face and the local feature information of the to-be-recognized image reflects detail information of each region on the face, more image detail information of a facial expression can be recovered by means of an effective combination of the local feature information and the global feature information.

Abstract: A pedestrian matching method includes: acquiring a first image and a second image; respectively extracting features of the first image and the second image to obtain a first local feature, a second local feature, a first high-order feature and a second high-order feature, the first local feature and the second local feature including local feature vectors corresponding to key points of a human body respectively; performing feature alignment on the first local feature and the second local feature to obtain a first fused feature and a second fused feature; performing feature alignment on the first high-order feature and the second high-order feature to obtain a first high-order fused feature and a second high-order fused feature; and determining, based on the first fused feature, the second fused feature, the first high-order fused feature and the second high-order fused feature, whether the first image and the second image include a same pedestrian.

Abstract: The provided are a face pose estimation method and apparatus, an electronic device, and a storage medium. The method includes: obtaining a target image containing face information, and inputting the target image into a pre-constructed pose estimation model; performing feature extraction on the target image by using a shallow densely connected layer to obtain a plurality of first feature maps containing shallow feature information; performing an information fusion operation on the plurality of first feature maps by using a deep feature multiplexing layer to obtain a second feature map, such that deep feature information is fused into the shallow feature information; and extracting face pose information in the second feature map by using an attention layer to obtain a third feature map containing the face pose information, performing prediction on the third feature map by using a classifier, and determining a face pose in the target image.