Abstract: Provided in embodiments of the present application are a method and device for age estimation. The method comprises: performing gender training with respect to a gender model on the basis of facial image samples so as to allow the gender model to converge, where the gender model comprises at least two convolution layers; performing age training with respect to an age model on the basis of the facial samples so as to allow the age model to converge, where the age model comprises the at least two convolution layers, the converged age model comprises the weights of the at least two convolution layers, and the weights of the at least two convolution layers that the converged gender model comprises; and performing age estimation with respect to an inputted facial image on the basis of the converged age model.

Abstract: A content recommendation method that includes: acquiring content cover images corresponding to multiple pieces of content accessed by a user account; acquiring cover image features of the multiple content cover images, and determining user account features of the user account according to the cover image features of the multiple content cover images; on the basis of cover image features of content to be recommended and the user account features, determining an access probability value of the user account accessing the content to be recommended; and providing, according to the access probability value, the content to be recommended to the user account.

Abstract: A human face image classification method includes: acquiring a human face image to be classified; inputting the human face image into a pre-set convolutional neural network model, and according to intermediate data output by a convolutional layer of the convolutional neural network model, identifying gender information of the human face image; and according to final data output by the convolutional layer of the convolutional neural network model, carrying out pre-set content understanding classification on the human face image in a range defined by the gender information, so that data regarding deciding a classification result output by the convolutional neural network model comprises a difference attribute for distinguishing between different genders.

Abstract: The disclosure relates to a method, an electronic device and a storage medium for segmenting an image. The method includes: obtaining an image to be segmented; determining a first face result by detecting a face in the image and a first key-point result by detecting one or more key-points of the face in the image; determining a first face region in the image based on the first face result and the second face result; and segmenting a hair region from the first face region by an image segmentation model, wherein the image segmentation model is trained based on sample images, label mask images and a probability distribution map of hair, and the label mask image comprises a hair region of the sample image, and the probability distribution map of hair comprises a probability distribution of hair in the sample images.

Abstract: A method and an apparatus for facial expression recognition is provided. A terminal device may obtain a face image by detecting an inputted image, determine expression classifications in the face image based on an expression classification standard, obtain expression coefficients of the expression classifications, and recognize expressions in the face image based on the expression coefficients.

Abstract: Provided in embodiments of the present application are a method and device for age estimation. The method comprises: performing gender training with respect to a gender model on the basis of facial image samples so as to allow the gender model to converge, where the gender model comprises at least two convolution layers; performing age training with respect to an age model on the basis of the facial samples so as to allow the age model to converge, where the age model comprises the at least two convolution layers, the converged age model comprises the weights of the at least two convolution layers, and the weights of the at least two convolution layers that the converged gender model comprises; and performing age estimation with respect to an inputted facial image on the basis of the converged age model.

Abstract: Disclosed are a human face image classification method and apparatus, and a server. The method comprises the following steps: acquiring a human face image to be classified; inputting the human face image into a pre-set convolutional neural network model, and according to intermediate data output by a convolutional layer of the convolutional neural network model, identifying gender information of the human face image; and according to final data output by the convolutional layer of the convolutional neural network model, carrying out pre-set content understanding classification on the human face image in a range defined by the gender information, so that data regarding deciding a classification result output by the convolutional neural network model comprises a difference attribute for distinguishing between different genders.

Abstract: The disclosure relates to a method, an electronic device and a storage medium for segmenting an image. The method includes: obtaining an image to be segmented; determining a first face result by detecting a face in the image and a first key-point result by detecting one or more key-points of the face in the image; determining a first face region in the image based on the first face result and the second face result; and segmenting a hair region from the first face region by an image segmentation model, wherein the image segmentation model is trained based on sample images, label mask images and a probability distribution map of hair, and the label mask image comprises a hair region of the sample image, and the probability distribution map of hair comprises a probability distribution of hair in the sample images.