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WO2018192246A1 - Procédé de détection d'émotion sans contact fondé sur une vision artificielle - Google Patents

Procédé de détection d'émotion sans contact fondé sur une vision artificielle Download PDF

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Publication number
WO2018192246A1
WO2018192246A1 PCT/CN2017/116060 CN2017116060W WO2018192246A1 WO 2018192246 A1 WO2018192246 A1 WO 2018192246A1 CN 2017116060 W CN2017116060 W CN 2017116060W WO 2018192246 A1 WO2018192246 A1 WO 2018192246A1
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image
face
processed
sequence
frame
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PCT/CN2017/116060
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English (en)
Chinese (zh)
Inventor
吕东岳
吕相文
刘弋锋
廖勇
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China Academy of Information and Communications Technology CAICT
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China Academy of Information and Communications Technology CAICT
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Priority claimed from CN201710256722.7A external-priority patent/CN107169419B/zh
Priority claimed from CN201710256725.0A external-priority patent/CN107153815A/zh
Application filed by China Academy of Information and Communications Technology CAICT filed Critical China Academy of Information and Communications Technology CAICT
Publication of WO2018192246A1 publication Critical patent/WO2018192246A1/fr
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition

Definitions

  • the present invention relates to the field of network technology application technologies, and in particular, to a non-contact emotion detection method based on machine vision.
  • Heart rate is an important indicator for clinical detection of life parameters.
  • the current method mainly uses contact detection technology.
  • the contact detection of biomedical signals refers to the direct or indirect contact with the human body through electrodes or sensors to achieve the purpose of detecting medical information.
  • the detection process In order to detect the inherent information of the human body (such as blood pressure, heart rate measurement, etc.) and information detection by means of external energy (X-ray, B-ultrasound detection, etc.), the detection process has certain constraints on the human body.
  • Non-contact detection refers to the use of external energy (detection medium), does not touch the human body, and at a certain distance, separated by a certain medium, through the detection of various micro-motion caused by human physiological activities, and thus obtain various physiological information.
  • Non-contact detection does not require too much restraint on the human body during the detection process, and the measurement process is more friendly. In some special occasions, it can achieve more concealed monitoring of human physiological characteristics, even in special criminal investigations. demand.
  • Non-contact heart rate detection technology can be divided into visual detection technology and non-visual detection technology according to the adopted methods; visual inspection technology is mainly based on imaging photoelectric plethysmography (IPPG), and non-visual detection technology is mainly photoelectric plethysmography (PPG) and radar detection technology based on Doppler principle (microwave, radio waves and sound waves, etc.).
  • IPPG imaging photoelectric plethysmography
  • PPG photoelectric plethysmography
  • radar detection technology based on Doppler principle (microwave, radio waves and sound waves, etc.).
  • the heart rate detection method based on machine vision has already appeared on the market.
  • the most famous is the "Life Sign Camera” software developed by Philips.
  • the workflow of the software is to capture the face image in the fixed area using the camera.
  • the color change of the facial image further obtains the value of the human heart rate.
  • the disadvantage of this method is that it needs to be protected by the tester during the measurement.
  • the face is kept fixed, so that the facial color change in the fixed area can be analyzed in this case.
  • This unfriendly use makes the subject experience poor in the process of use. In some special situations (such as trials, polygraphs, etc.) that cannot actively request the subject to remain fixed, they cannot be used normally.
  • One aspect of the object of the present invention is to provide a machine vision-based non-contact emotion detection method, characterized in that the method comprises:
  • determining a face position in each frame of the video image in the image sequence to be processed by using a preset face detection algorithm and a image stabilization algorithm includes:
  • the preliminary face position is corrected by a preset image stabilization algorithm to obtain a face position of each frame of image.
  • the image sequence to be processed is normalized according to the face position in each frame of the video image, and the face image is extracted in the normalized image sequence to be processed, according to the The face image obtains a partial image matrix of the face, including:
  • a facial partial image matrix is obtained according to the adjusted face image.
  • the color variation of the partial image matrix of the face is enlarged by using a preset video enlargement algorithm to obtain an enlarged partial image sequence of the face, including:
  • the enlarged partial information is embedded in the facial partial image sequence by an upsampling process of the same level as the multi-layer downsampling, to obtain an enlarged partial image sequence of the face.
  • the RGB mean value of each frame image in the enlarged partial image sequence of the face is counted, and the human body sign is obtained according to the change of the mean value.
  • Another aspect of the present invention provides a machine vision-based non-contact emotion detecting apparatus, including:
  • a pre-processing module configured to acquire a video image that includes face information, and combine the video image with a plurality of pre-stored video images to obtain a sequence of images to be processed;
  • a face detection module configured to determine, by using a preset face detection algorithm and a image stabilization algorithm, a face position in each frame of the video image in the image sequence to be processed;
  • the facial partial image determining module normalizes the image sequence to be processed according to the face position in each frame of the video image, and extracts the face image in the normalized image sequence to be processed. And obtaining a facial partial image matrix according to the face image;
  • An amplifying module configured to enlarge a color change of the partial image matrix of the face by using a preset video enlargement algorithm, to obtain an enlarged partial image sequence of the face;
  • the signal processing module is configured to perform signal processing on the enlarged partial image sequence of the face to obtain a human body sign, and detect an emotional activity of the human body according to the human body feature.
  • the face detection module is specifically configured to:
  • the preliminary face position is corrected by a preset image stabilization algorithm to obtain a face position of each frame of image.
  • the facial partial image determining module is specifically configured to:
  • a facial partial image matrix is obtained according to the adjusted face image.
  • the amplification module is specifically configured to:
  • the enlarged partial information is embedded in the facial partial image sequence by an upsampling process of the same level as the multi-layer downsampling, to obtain an enlarged partial image sequence of the face.
  • the signal processing module calculates an RGB mean value of each frame image in the enlarged partial image sequence of the face, and obtains the human body sign according to the change of the mean value.
  • a real-time face video is captured by using a common webcam or a mobile phone camera, and the face position is determined by a face detection algorithm and a image stabilization algorithm, and the color change of the partial image matrix of the face is amplified by a video enlargement algorithm, and the signal is passed.
  • the processing algorithm obtains an accurate real-time human heart rate value, and detects the emotional activity of the human body according to the human heart rate value.
  • the present invention introduces a face detection and image stabilization module, so that the subject can use a movable head with a small amplitude within the camera shooting range, and The heart rate value of the subject can be accurately obtained, and the emotional activity of the subject is detected according to the heart rate value.
  • FIG. 1 is a flow chart schematically showing a machine vision based non-contact emotion detection method of an embodiment of the method of the present invention
  • FIG. 2 is a block diagram showing the structure of a machine vision-based non-contact emotion detecting device of an embodiment of the apparatus of the present invention
  • FIG. 3 is a block diagram showing the structure of the non-contact emotion detecting device based on the machine vision of the example 1.
  • the present invention provides a non-contact emotion detection method and apparatus based on machine vision.
  • the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
  • FIG. 1 is a flowchart of a machine vision based non-contact emotion detection method according to an embodiment of the method of the present invention.
  • the body signs include heart rate, blood pressure, respiratory rate, etc., and detect human emotions according to specific human characteristics.
  • a machine vision based non-contact emotion detection method according to an embodiment of the method of the present invention includes the following processing:
  • Step 101 Acquire a video image including face information, and combine the video image with a plurality of frame video images stored in advance to obtain a sequence of images to be processed.
  • Step 102 Determine, by using a preset face detection algorithm and a image stabilization algorithm, a face position in each frame of the video image in the image sequence to be processed.
  • determining, by using a preset face detection algorithm and a image stabilization algorithm, a face position in each frame of the video image in the image sequence to be processed including:
  • the preliminary face position is corrected by a preset image stabilization algorithm to obtain a face position of each frame of image.
  • Step 103 normalize the image sequence to be processed according to the face position in each frame of the video image, and extract a face image in the normalized image sequence to be processed, according to the The face image obtains a partial image matrix of the face.
  • the image sequence to be processed is normalized according to the face position in each frame of the video image, and the face image is extracted in the normalized image sequence to be processed, according to the The face image obtains a partial image matrix of the face, including:
  • a facial partial image matrix is obtained according to the adjusted face image.
  • Step 104 The color variation of the partial image matrix of the face is enlarged by using a preset video enlargement algorithm to obtain an enlarged partial image sequence of the face.
  • the color variation of the partial image matrix of the face is enlarged by using a preset video enlargement algorithm, and the enlarged partial image sequence of the face is obtained, including:
  • the enlarged partial information is embedded in the facial partial image sequence by an upsampling process of the same level as the multi-layer downsampling, to obtain an enlarged partial image sequence of the face.
  • Step 105 Perform signal processing on the enlarged partial image sequence of the face to obtain a human body sign. Specifically, the RGB mean value of each frame image in the partial image sequence of the face after the statistical enlargement is obtained according to the change of the mean value.
  • FIG. 2 is a schematic structural diagram of a machine vision-based non-contact emotion detecting device according to an embodiment of the present invention.
  • the machine vision-based non-contact emotion detecting apparatus includes: a pre-processing module 20, a face detecting module 22, a facial partial image determining module 24, an amplifying module 26, and a signal processing module 28.
  • a pre-processing module 20 includes: a face detecting module 22, a facial partial image determining module 24, an amplifying module 26, and a signal processing module 28.
  • the pre-processing module 20 is configured to acquire a video image including face information, and combine the video image with a plurality of frame video images stored in advance to obtain a sequence of images to be processed.
  • the face detection module 22 is configured to determine, by using a preset face detection algorithm and a image stabilization algorithm, a face position in each frame of the video image in the image sequence to be processed.
  • the face detection module 22 is specifically configured to:
  • the facial partial image determining module 24 normalizes the image sequence to be processed according to the face position in each frame of the video image, and extracts a person in the normalized image sequence to be processed. a face image, and a facial partial image matrix is obtained according to the face image.
  • the facial partial image determining module 24 is specifically configured to:
  • a facial partial image matrix is obtained according to the adjusted face image.
  • the amplifying module 26 is configured to enlarge a color change of the partial image matrix of the face by using a preset video enlargement algorithm to obtain an enlarged partial image sequence of the face.
  • the amplification module 26 is specifically configured to:
  • the enlarged partial information is embedded in the facial partial image sequence by an upsampling process of the same level as the multi-layer downsampling, to obtain an enlarged partial image sequence of the face.
  • the signal processing module 28 is configured to perform signal processing on the enlarged partial image sequence of the face to obtain a human body sign.
  • the RGB mean value of each frame of the image in the partial image sequence of the face after the quantization is obtained, and the human body sign is obtained according to the change of the mean value.
  • an exemplary example 1 is given with heart rate as a human body sign, and the human mood change is detected according to the heart rate.
  • FIG. 3 is a schematic structural diagram of a machine vision-based non-contact emotion detecting device according to Example 1, as shown in FIG. 3, a machine vision-based non-contact emotion detecting device having a video image taken Like the device 31 and the computing device 32 for video image processing, the video image capturing device 31 takes a video stream to the face portion 30.
  • the computing device 32 includes a computing chip (eg, a central processing unit CPU) and a memory (eg, a solid state drive SSD) that stores computing chip execution instructions.
  • the computing chip of computing device 32 includes the following four modules:
  • Module 1 Video Input Module
  • the function of this module is to obtain a video stream from the camera device.
  • Module 2 Video Preprocessing Module
  • the function of the module is to preprocess the video stream obtained by the module, and obtain a partial image matrix of the face that the module 3 can process.
  • This module contains three submodules:
  • Sub-module one face detection
  • the module After receiving the video stream delivered by the module, it first merges with the previous several frames of images stored in the memory to obtain a sequence of images to be processed.
  • the face of each frame in the image sequence after the combination is detected, the preliminary face position information is obtained, and the face position is finely corrected by the optical flow stabilization algorithm to obtain the optimized face position information.
  • Submodule 2 Lighting adjustment
  • the feature points extraction algorithm (for example, SIFT, SURF, ORB, etc.) is used to extract the feature points of each frame of the image sequence, and the brightness of the feature points that are not corresponding to the face region is normalized to obtain the brightness of each frame image.
  • the coefficients are normalized to achieve illumination normalization of the image sequence.
  • Submodule 3 Area Determination
  • the image pixel composition corresponding to the two parts of the forehead and the cheek is selected to be a matrix sequence for subsequent processing by the module 3.
  • the specific method is to first resize the face image extracted from each frame of the image sequence, so that all face images are the same size, and then select the pixels of the forehead and cheek regions of the face to be combined into a matrix that can be processed by the module three. sequence.
  • the function of the module is to process the facial partial image matrix obtained by the module 2, and obtain the human heart rate information that the module 4 can process.
  • This module contains two submodules:
  • Submodule 1 Video Zoom
  • the matrix After receiving the partial image matrix of the face transmitted by the module 2, the matrix is processed by the video amplification algorithm to effectively enlarge the facial color change of the forehead and the cheek of the face, specifically
  • the method is to perform multi-layer downsampling on the matrix sequence, perform band-pass filtering on the final down-sampling result, multiply the filtered result by multiplying the amplification factor, and zoom in after the upsampling process of the same level as the downsampling.
  • the information is embedded in the image corresponding to the forehead and the cheek portion, and the enlarged partial image sequence of the face is obtained.
  • Submodule 2 Signal Processing
  • Signal processing is performed on the partial image sequence of the face after the face color is enlarged, and an accurate human heart rate value is obtained. Specifically, the RGB mean value of each frame image in the enlarged partial image sequence of the face is counted, and the human heart rate value is obtained according to the change of the mean value.
  • the function of the module is to output the heart rate information of the test subject, and can adopt various methods, for example, displaying the heart rate curve of the test subject, the heart rate value, and the like in the visual interface. Reflects the emotional activity of the human body according to the heart rate value. For example, when the heart rate reaches a certain value, the human emotion is anxiety and nervousness; when the heart rate is lower than a certain value, the human emotion is sad and depressed.
  • the processing flow of the present invention includes the following seven steps:
  • Step 1 The module receives the video stream of the face image captured by the camera device, and the camera device can be connected to the computer for collecting video information by using various connection methods such as USB connection and local area network connection.
  • a computer for collecting video information collects and parses the video stream through a software tool.
  • the computer for collecting video information acquires the video stream and saves it into the matrix memory that can be processed by module two, and passes it to module two.
  • Step 2 After receiving the matrix memory transmitted by the module, the submodule of the module 2 first combines with the previous several frames of images stored in the memory to obtain an image sequence, and detects the face of each frame of the image sequence to obtain a preliminary person. Face location information. The position of the face is finely corrected by the optical flow image stabilization algorithm to obtain the optimized face position information. The image sequence and the optimized face position information are transmitted to the submodule 2 of the module 2.
  • Step 3 After the sub-module 2 of the module 2 receives the image sequence transmitted by the sub-module of the module 2 and the optimized face position information, the feature point extraction algorithm is used to extract the feature points of each frame of the image sequence, and the optimized person is combined.
  • the face position information normalizes the brightness of the feature points of the corresponding positions not belonging to the face region, and obtains the brightness normalization coefficient of each frame image, thereby realizing the illumination normalization process of the image sequence.
  • the image sequence normalized by illumination and the optimized face position information are transmitted to submodule 3 of module 2.
  • Step 4 Submodule 3 of module 2 receives the illuminated light of submodule 2 of module 2 After the normalized image sequence and the optimized face position information, the face image extracted according to the optimized face position information in each frame of the image sequence is resized so that all face images are the same size. Secondly, the pixel transformation of the two parts of the forehead and the cheek is selected as a partial image matrix of the face. Pass the facial partial image matrix to module three.
  • Step 5 After receiving the partial image matrix of the face transmitted by the module 2, the sub-module of the module 3 processes the matrix by using a video amplification algorithm to obtain a partial image sequence of the face. Pass the partial image sequence of the face to submodule 2 of module 3.
  • Step 6 After the sub-module 2 of the module 3 receives the partial image sequence of the face transmitted by the sub-module of the module 3, the average value of each frame image in the partial image sequence of the face (the image is RGB mode) is obtained, and the corresponding heart rate is obtained through processing. Value, passing the heart rate value to module four.
  • Step 7 After receiving the heart rate value transmitted by the module three, the module 4 outputs the heart rate value in a visual manner, and reflects the emotional activity of the human body through the heart rate value.
  • the invention can obtain the heart rate value normally when the face of the subject is moved, and obtains a stable face image by using the face detection algorithm and the image stabilization algorithm on the basis of obtaining the face video by the camera device, and solving other methods in the method. Measurement failure problems caused by free movement of the human head in video heart rate measurement.
  • the camera device webcam, surveillance camera, etc. captures the face video of the indoor subject in a natural lighting environment, and the subject's head moves slightly when shooting, and the subject is output in real time.
  • Heart rate value The invention solves the problem of detecting heart rate of an active human body in a non-contact state.
  • Real-time face video is captured by a common webcam or mobile phone camera, the face position is determined by the face detection algorithm and the image stabilization algorithm, and the color change of the sensitive area of the face is amplified by the video enlargement algorithm, which is accurately obtained by the signal processing algorithm.
  • Real-time human heart rate values Real-time human heart rate values.
  • the present invention introduces a face detection and image stabilization module, so that the subject can use a movable head with a small amplitude within the camera shooting range, and The heart rate value of the subject can be accurately obtained, and the emotional activity of the person is accurately detected according to the heart rate value.

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Abstract

L'invention concerne un procédé et un appareil de détection d'émotion sans contact fondés sur une vision artificielle. Le procédé consiste : à acquérir une image vidéo contenant des informations de visage humain, de façon à obtenir une séquence d'images à traiter; à déterminer une position de visage humain à chaque trame d'une image vidéo dans la séquence d'images à traiter au moyen d'un algorithme de détection de visage humain prédéfini et d'un algorithme de stabilisation d'image; à effectuer un traitement de normalisation sur la séquence d'images à traiter, à extraire une image de visage humain de la séquence d'images à traiter après le traitement de normalisation, et à obtenir également une matrice d'image locale de visage; et à amplifier un changement de couleur dans la matrice d'image locale de visage à l'aide d'un algorithme d'amplification vidéo prédéfini, et à effectuer un traitement de signal sur la séquence d'images locales de visage amplifiée, de façon à obtenir des signes corporels humains. Dans la présente invention, un module de détection de visage humain et un module de stabilisation d'image sont introduits, de telle sorte qu'un sujet puisse déplacer sa tête de façon modérée dans la plage de tir d'une caméra pendant l'utilisation, et que la valeur de fréquence cardiaque du sujet puisse être obtenue avec précision.
PCT/CN2017/116060 2017-04-19 2017-12-14 Procédé de détection d'émotion sans contact fondé sur une vision artificielle Ceased WO2018192246A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
CN201710256722.7A CN107169419B (zh) 2017-04-19 2017-04-19 基于机器视觉的非接触式人体体征检测方法及装置
CN201710256722.7 2017-04-19
CN201710256725.0A CN107153815A (zh) 2017-04-19 2017-04-19 一种身份验证方法、设备及存储介质
CN201710256725.0 2017-04-19

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CN116831581A (zh) * 2023-06-15 2023-10-03 中南大学 一种基于远程生理体征提取的驾驶员状态监测方法及系统
FR3146009A1 (fr) 2022-12-31 2024-08-23 Anavid France Système, procédé et dispositif de détection automatique et en temps réel de satisfaction des visiteurs à un établissement recevant du public (ERP)
WO2024212462A1 (fr) * 2023-04-10 2024-10-17 中国科学院自动化研究所 Procédé et systèmes de détection d'état psychologique, et support de stockage lisible

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CN107169419A (zh) * 2017-04-19 2017-09-15 中国电子科技集团公司电子科学研究院 基于机器视觉的非接触式人体体征检测方法及装置

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR3146009A1 (fr) 2022-12-31 2024-08-23 Anavid France Système, procédé et dispositif de détection automatique et en temps réel de satisfaction des visiteurs à un établissement recevant du public (ERP)
WO2024212462A1 (fr) * 2023-04-10 2024-10-17 中国科学院自动化研究所 Procédé et systèmes de détection d'état psychologique, et support de stockage lisible
CN116831581A (zh) * 2023-06-15 2023-10-03 中南大学 一种基于远程生理体征提取的驾驶员状态监测方法及系统

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