Patent ID: 12198468

DETAILED DESCRIPTION OF THE EMBODIMENTS

The technical scheme in the embodiments of the disclosure will be clearly and completely described below in combination with the attached drawings in the embodiments of the disclosure. Obviously, the described embodiments are only part of the embodiments of the disclosure, not all of the embodiments.

Embodiment 1

Referring toFIG.1andFIG.2, a non-contact facial blood pressure measurement method based on 3D CNN includes the following steps.S110: collecting an actual face video sample and training a blood pressure prediction model based on face images using 3D CNN neural network.S120: obtaining a face video in real time through a HD camera.S130: recognizing face key points in the face video obtained in S120through dlib face recognition model, selecting a face region of interest, and extracting face images from the region.S140: performing a wavelet transform operation on the face images extracted in S130to remove noise.S150: inputting seven consecutive frames of the face images into the 3D CNN blood pressure prediction model trained in S110to obtain a blood pressure value of the measured person.

In S120, when recording the face video of the measured person through the HD camera, the face of the measured person needs to be completely unobstructed. The face video recording needs to be carried out in a bright and stable environment. And at the same time, the face receives light evenly, and there is no obvious dark light area on the face. During the face video recording, the measured person shall keep the body stable, the head shall not shake or tremble, and the face shall be facing the camera until a set collecting time is reached. And in case of large shaking, the recording shall be carried out again.

The blood pressure value of the measured person obtained in S150includes systolic blood pressure and diastolic blood pressure. The obtained blood pressure value is compared with a normal range of the blood pressure to judge whether the blood pressure value of the measured person is in the normal range.

Embodiment 2

The embodiment 2 is based on embodiment 1, but the difference is as follows.

The training 3D CNN blood pressure prediction model in S110includes the following steps.A1: recording a face video through a HD camera.A2: obtaining real-time blood pressure values through a cuff electronic sphygmomanometer.A3: detecting face key points in the face video obtained in A1, selecting a region of interest, and extracting face images of the region of interest.A4: preprocessing images, that is, performing a wavelet transform operation on the face images extracted in A3to remove noise, inputting seven consecutive frames of the face images and the corresponding real blood pressure values into a constructed 3D CNN model for training, then training the model based on a mean square error loss function, and finally obtaining the 3D CNN blood pressure prediction model.

Specifically, the first layer of 3D CNN architecture is the hardwired layer, which processes the original frames to generate signals of multiple channels, then processes the multiple channels respectively, and finally combines the information of all channels to obtain the final features.

The information of three channels is extracted from each frame, which are grayscale, gradient in x and y directions. The three channels of grayscale, gradients in x and y directions can be calculated per frame, and each channel is convolved using the set convolution kernel to extract different features.

The disclosure makes full use of the function of 3D convolution operation to extract spatial and temporal features from video data for action recognition. By using the 3D feature extractor to operate in the spatial and temporal dimensions, the motion information of multiple consecutive frames in the face video stream is captured.

The disclosure sets a 3D convolution neural network architecture based on 3D convolution feature extraction. The 3D CNN architecture generates multiple information channels from adjacent video frames, performs convolution and down sampling in each channel respectively, and obtains the final feature representation by combining the information from all channels.

Embodiment 3

The embodiment 3 is based on embodiment 1 and 2, but the difference is as follows.

The extracting face images in S130includes the following steps.B1: detecting four coordinate extreme values of the face in each frame of the face images through dlib face recognition model to determine a position of the face.B2: detecting 68 key points of the face, wherein positions of the key points includes chin, eyes, nose, mouth and other regions; and drawing an overall contour of the face through the key points.B3: determine the region of interest through the key points of the face, which includes the left and right cheeks, forehead, human middle, chin and nasal wing, and extracting and saving an image with a size of 50×50 in each the region of interest.

The disclosure uses dlib face recognition model to detect the key points of each frame image of the face video, and prevents the position movement of the region of interest caused by face movement or body shaking.

Embodiment 4

The embodiment 4 is based on embodiment 1 to 3, but the difference is as follows.

The performing a wavelet transform operation on the face images in S140includes the following steps.C1: performing a wavelet transform on the images.C2: performing a threshold quantization on high-frequency coefficients after hierarchical decomposition.C3: reconstructing image signals by two-dimensional wavelet.

The above is only the preferred specific embodiments of the disclosure, but the protection scope of the disclosure is not limited to this. Within the technical scope disclosed by the disclosure, the equivalent replacement or change implemented according to the technical scheme and inventive concept of the disclosure by any technician familiar with the technical field shall be covered by the protection scope of the disclosure.