Abstract: The present disclosure provides a method and apparatus for facial classification, which is applied to the field of image processing. The method includes acquiring a color image of a target face, where the color image includes information of at least one channel, inputting the information into a neural network, and classifying, by the neural network, the target face according to the information and a first parameter. The first parameter includes at least one facial category and first parameter data for identifying a facial category of the target face. The method and device of the present disclosure can analyze facial attributes from the content of a facial image, and automatically classify many facial images according to their facial attributes. This reduces the burden of manual classification, thereby allowing facial images to be stored in a clear and orderly fashion and improving classification efficiency.

Abstract: The present disclosure provides a method and apparatus for expression recognition, which is applied to the field of image processing. The method includes acquiring a three-dimensional image of a target face and a two-dimensional image of the target face, where the three-dimensional image includes first depth information of the target face and first color information of the target face, and the two-dimensional image includes second color information of the target face. A first neural network classifies an expression of the target face according to the first depth information, the first color information, the second color information, and a first parameter to the target face. The first parameter includes at least one facial expression category and first parameter data for identifying an expression category of the target facial. The disclosed method and device can accurately recognize facial expressions under different facial positions and different illumination conditions.

Abstract: The present disclosure provides a method and apparatus for expression recognition, which is applied to the field of image processing. The method includes acquiring a three-dimensional image of a target face and a two-dimensional image of the target face, where the three-dimensional image includes first depth information of the target face and first color information of the target face, and the two-dimensional image includes second color information of the target face. A first neural network classifies an expression of the target face according to the first depth information, the first color information, the second color information, and a first parameter to the target face. The first parameter includes at least one facial expression category and first parameter data for identifying an expression category of the target facial. The disclosed method and device can accurately recognize facial expressions under different facial positions and different illumination conditions.

Abstract: The present disclosure provides a method and apparatus for facial classification, which is applied to the field of image processing. The method includes acquiring a color image of a target face, where the color image includes information of at least one channel, inputting the information into a neural network, and classifying, by the neural network, the target face according to the information and a first parameter. The first parameter includes at least one facial category and first parameter data for identifying a facial category of the target face. The method and device of the present disclosure can analyze facial attributes from the content of a facial image, and automatically classify many facial images according to their facial attributes. This reduces the burden of manual classification, thereby allowing facial images to be stored in a clear and orderly fashion and improving classification efficiency.

Abstract: The present disclosure provides a method and apparatus for expression recognition, which is applied to the field of image processing. The method includes acquiring a three-dimensional image of a target face and a two-dimensional image of the target face, where the three-dimensional image includes first depth information of the target face and first color information of the target face, and the two-dimensional image includes second color information of the target face. A first neural network classifies an expression of the target face according to the first depth information, the first color information, the second color information, and a first parameter to the target face. The first parameter includes at least one facial expression category and first parameter data for identifying an expression category of the target facial. The disclosed method and device can accurately recognize facial expressions under different facial positions and different illumination conditions.

Abstract: The present disclosure provides a method and apparatus for facial classification, which is applied to the field of image processing. The method includes acquiring a color image of a target face, where the color image includes information of at least one channel, inputting the information into a neural network, and classifying, by the neural network, the target face according to the information and a first parameter. The first parameter includes at least one facial category and first parameter data for identifying a facial category of the target face. The method and device of the present disclosure can analyze facial attributes from the content of a facial image, and automatically classify many facial images according to their facial attributes. This reduces the burden of manual classification, thereby allowing facial images to be stored in a clear and orderly fashion and improving classification efficiency.