Face attribute recognition method, device, terminal device, and storage medium
A technology of attribute recognition and terminal equipment, applied in the computer field, can solve the problem of large errors in age estimation and gender detection, and achieve the effect of improving age estimation and gender detection, high accuracy, and solving degradation problems.
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no. 1 example
[0042] see figure 1 , figure 1 It is a schematic flowchart of a face attribute recognition method provided in the first embodiment of the present invention. The present invention provides a face attribute recognition method, specifically comprising:
[0043] S10. Obtain the current data frame in the video stream.
[0044] In this embodiment, the video stream is composed of consecutive pictures, and each picture is a frame. Simply put, the number of frames is the number of frames of pictures transmitted in 1 second, and it can also be It is understood that the graphics processor can refresh several times per second, usually expressed in fps (Frames Per Second). Each frame is a still image, and displaying frames in rapid succession creates the illusion of motion. In the present embodiment, adopt common CMOS camera to carry out video acquisition, use opencv to extract image from camera, and do tracking test, image resolution adopts CIF format (352*288) 24Bit true color, and l...
no. 2 example
[0064] The second embodiment of the present invention provides a device for identifying human face attributes by a robot, including:
[0065] Obtaining module 10, used to obtain the current data frame in the video stream;
[0066] Capture module 20, is used for capturing human face from described current data frame, and obtains human face region image;
[0067] Extraction module 30, is used for extracting the characteristic information of people's face according to described human face area image; And described characteristic information input convolutional neural network model; Wherein, described convolutional neural network model is based on ResNet architecture;
[0068] The output module 40 is configured to obtain the age and gender corresponding to the face according to the output result of the convolutional neural network model.
[0069] Further, the capture module 20 specifically includes:
[0070] A conversion module for converting the face area image into a grayscale...
no. 3 example
[0079]The third embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processing, when the processor executes the computer program, the present invention is implemented. The face attribute recognition method described in the first embodiment above.
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