WO2019033576A1 - 人脸姿态检测方法、装置及存储介质 - Google Patents
人脸姿态检测方法、装置及存储介质 Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/165—Detection; Localisation; Normalisation using facial parts and geometric relationships
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30196—Human being; Person
- G06T2207/30201—Face
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30244—Camera pose
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- the present application relates to the field of computer vision processing technologies, and in particular, to a face gesture detection method, apparatus, and storage medium.
- Computer vision processing technology began in the early 1960s and includes multiple research directions such as detection, tracking, and recognition. With the rapid development of computer and network technology, computer vision processing technology has been widely used in many important fields such as industrial inspection, security monitoring, clinical diagnosis and treatment, transportation, entertainment, and military. Among them, the computer face gesture detection technology can be used for intelligent systems such as assisted driving and human-computer interaction, and has broad application prospects.
- the existing face attitude detection system mostly uses depth learning to directly train face sample data of different poses, and then uses the trained model to judge the face pose, but this detection method requires a large number of samples and accuracy. Poor, and can only detect the trained posture, can not detect all face gestures.
- the present invention provides a face gesture detection method, device and storage medium, the main purpose of which is to calculate face pose information according to coordinate values of key feature points of a human face, improve the speed and accuracy of face gesture detection, and realize arbitrary angles. Face gesture detection.
- the present application provides a method for detecting a face gesture, the method comprising:
- Face feature point extraction step extracting N face feature points from the face image by a face detection algorithm
- a key feature point extraction step extracting, from the N facial feature points, a first feature point near the right ear, a second feature point near the right ear, and a third feature near the lower ear Pointing, near the fourth feature point above the left ear, and extracting the nose point feature point as the fifth feature point;
- a first calculating step forming the fifth feature point and the first feature point into a first vector, the fifth feature point and the fourth feature point forming a second vector, and calculating a first angle between the first vector and the second vector, Determining, according to a change of the first angle and the initial angle, a rotation angle of the face in the face image about the X axis;
- a second calculating step forming the first feature point and the fourth feature point into a third vector, and extracting a line L1 parallel to the X axis from the fourth feature point, and calculating a second angle between the third vector and the straight line L1, Determining, according to the second angle, a rotation angle of the face in the face image about the Y axis;
- a third calculating step the distance between the second feature point and the fifth feature point is recorded as a first distance, and the distance between the third feature point and the fifth feature point is recorded as a second distance, and the first distance and the second distance are calculated of The ratio determines a rotation direction of the face in the face image about the Z axis according to a ratio of the first distance to the second distance.
- the present application further provides a computing device including an image capturing device, a memory, and a processor, wherein the memory includes a face gesture detecting program, and the face pose detecting program is executed by the processor:
- Face feature point extraction step extracting N face feature points from the face image by a face detection algorithm
- a key feature point extraction step extracting, from the N facial feature points, a first feature point near the right ear, a second feature point near the right ear, and a third feature near the lower ear Pointing, near the fourth feature point above the left ear, and extracting the nose point feature point as the fifth feature point;
- a first calculating step forming the fifth feature point and the first feature point into a first vector, the fifth feature point and the fourth feature point forming a second vector, and calculating a first angle between the first vector and the second vector, Determining, according to a change of the first angle and the initial angle, a rotation angle of the face in the face image about the X axis;
- a second calculating step forming the first feature point and the fourth feature point into a third vector, and extracting a line L1 parallel to the X axis from the fourth feature point, and calculating a second angle between the third vector and the straight line L1, Determining, according to the second angle, a rotation angle of the face in the face image about the Y axis;
- a third calculating step the distance between the second feature point and the fifth feature point is recorded as a first distance, and the distance between the third feature point and the fifth feature point is recorded as a second distance, and the first distance and the second distance are calculated The ratio of the first distance to the second distance determines the direction of rotation of the face in the face image about the Z axis.
- the present application further provides a computer readable storage medium including a face gesture detection program.
- a face pose detection program When the face pose detection program is executed by the processor, any step in the face pose detection method as described above is implemented.
- the face gesture detection method, device and computer readable storage medium proposed by the present application, by extracting key feature points of a human face, calculating posture information such as a face rotation angle according to coordinate values of the key feature points, and improving face gesture detection Speed and accuracy for face detection at any angle.
- FIG. 1 is a schematic diagram of an operating environment of a preferred embodiment of a computing device of the present application
- FIG. 2 is a functional block diagram of the face pose detection program of FIG. 1;
- FIG. 3 is a schematic diagram of a face feature point extracted by the extraction module of FIG. 2;
- FIG. 4 is a flow chart of a preferred embodiment of the applicant's face gesture detecting method
- FIG. 5 is a schematic diagram of feature points, vectors, and angles involved in step S30 of FIG. 4; FIG.
- FIG. 6 is a schematic diagram of feature points, vectors, straight lines, and included angles in step S40 of FIG. 4;
- FIG. 7 is a schematic diagram of a feature point and a distance between feature points involved in step S50 in FIG. 4;
- FIG. 1 it is a schematic diagram of an operating environment of a preferred embodiment of the computing device 1 of the present application.
- the computing device 1 may be a terminal device having a computing function such as a server, a smart phone, a tablet computer, a portable computer, or a desktop computer.
- the computing device 1 includes an imaging device 11, a memory 12, a processor 13, a network interface 14, and a communication bus 15.
- the camera device 11 is installed in a specific place, such as an office space, a monitoring area, and a smart car cab.
- a real-time image is captured in real time for a target entering the specific place, and the captured real-time image is transmitted to the processor 13 through the network.
- Network interface 14 may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface).
- Communication bus 15 is used to implement connection communication between these components.
- Memory 12 includes at least one type of readable storage medium.
- the at least one type of readable storage medium may be a non-volatile storage medium such as a flash memory, a hard disk, a multimedia card, a card type memory, or the like.
- the readable storage medium may be an internal storage unit of the computing device 1, such as a hard disk of the computing device 1.
- the readable storage medium may also be an external memory of the computing device 1, such as a plug-in hard disk (Smart Media Card, SMC) equipped on the computing device 1, Secure Digital (SD) card, Flash Card, etc.
- Smart Media Card Smart Media Card, SMC
- SD Secure Digital
- the readable storage medium of the memory 12 is generally used to store the face pose detection program 10 installed in the computing device 1, the real-time image obtained by the camera device 11, the face image, the initial data, and the like.
- the memory 12 can also be used to temporarily store data that has been output or is about to be output.
- the processor 13 may be a Central Processing Unit (CPU), microprocessor or other data processing chip for running program code or processing data stored in the memory 12, such as executing a human face.
- the attitude detection program 10 and the like.
- Figure 1 shows only computing device 1 having components 11-15 and face pose detection program 10, but it should be understood that not all illustrated components may be implemented and that more or fewer components may be implemented instead. .
- the computing device 1 may further include a user interface
- the user interface may include an input unit such as a keyboard, a voice input device such as a microphone, a device with a voice recognition function, a voice output device such as an audio, a headphone, and the like.
- the user interface may also include a standard wired interface and a wireless interface.
- the computing device 1 may also include a display, which may also be referred to as a display screen or display unit as appropriate.
- a display may also be referred to as a display screen or display unit as appropriate.
- it may be an LED display, a liquid crystal display, or a touch Liquid crystal display, OLED (Organic Light-Emitting Diode) touch sensor, and the like.
- the display is used to display information processed in the computing device 1 and a user interface for displaying visualizations.
- the computing device 1 further comprises a touch sensor.
- the area provided by the touch sensor for the user to perform a touch operation is referred to as a touch area.
- the touch sensor described herein may be a resistive touch sensor, a capacitive touch sensor, or the like.
- the touch sensor includes not only a contact type touch sensor but also a proximity type touch sensor or the like.
- the touch sensor may be a single sensor or a plurality of sensors arranged, for example, in an array.
- the area of the display of the computing device 1 may be the same as or different from the area of the touch sensor.
- a display is stacked with the touch sensor to form a touch display.
- the computing device detects a user-triggered touch operation based on the touch display.
- the computing device 1 may further include an RF (Radio Frequency) circuit, a sensor, an audio circuit, and the like, and details are not described herein.
- RF Radio Frequency
- the memory 12 as a computer readable storage medium may include data such as an operating system, a face gesture detecting program 10, a real-time image obtained by the camera 11, and a face image.
- the processor 13 executes the face gesture detecting program 10 stored in the memory 12, the following steps are performed:
- a key feature point extraction step extracting, from the N facial feature points, a first feature point P1 near the right ear, a second feature point P3 near the right ear, and a part near the left ear a third feature point P15, a fourth feature point P17 near the left ear, and extracting a nose point feature point as a fifth feature point P31;
- a first calculating step forming the fifth feature point P31 and the first feature point P1 into a first vector, and the fifth feature point P31 and the fourth feature point P17 form a second vector (refer to FIG. 5), and calculating the first a first angle between the vector and the second vector, determining a rotation angle of the face in the face image about the X axis according to the change of the first angle and the initial angle;
- a second calculating step forming the first feature point P1 and the fourth feature point P17 into a third vector, and extracting a line L1 parallel to the X axis from the fourth feature point P17 (refer to FIG. 6), and calculating the third a second angle between the vector and the straight line L1, and determining, according to the second angle, a rotation angle of the face in the face image about the Y axis;
- a third calculating step the distance between the second feature point P3 and the fifth feature point P31 is recorded as a first distance, and the distance between the third feature point P15 and the fifth feature point P31 is recorded as a second distance (refer to FIG. 7
- the ratio of the first distance to the second distance is calculated, and the rotation direction of the face around the Z axis in the face image is determined according to the ratio of the first distance to the second distance.
- the first calculating step includes:
- the first angle is greater than 120°, it indicates that the face in the face image rotates around the X axis, and the rotation angle is the difference between the first angle and the initial angle;
- the first angle is less than 120°, it indicates that the face in the face image is rotated downward about the X axis, and the rotation angle is the difference between the initial angle and the first angle;
- the first angle is equal to 120°, it indicates that the face in the face image is in an initial position with respect to the X axis, that is, not raised or bowed.
- the second calculating step includes:
- the second angle When the second angle is 0°, it indicates that the face in the face image is in an initial position with respect to the Y axis, that is, the head is not left or right;
- the third calculating step includes:
- the ratio of the first distance to the second distance is less than 1, it indicates that the face in the face image is rotated right around the Z axis.
- the face detection algorithm is a dlib face feature point detection algorithm.
- 68 feature points are extracted from the face image by using the dlib face feature point detection algorithm, including 17 facial contour feature points, 10 eyebrow feature points, and 4 nose wing feature points, including the tip of the nose.
- the facial contour feature points the first feature point near the right ear is symmetric with the fourth feature point near the left ear, and the second feature point near the right ear is symmetric with the third feature point near the left ear.
- the X, Y, and Z coordinate axes are defined as: the face is considered to be a cylinder, and the central axis of the cylinder is used as the vertical coordinate axis Z axis of the face data, and the nose point feature point is taken and the cylindrical center axis is taken.
- a straight line intersecting perpendicularly is taken as the Y axis, and a cross direction of the Z axis and the Y axis is taken as the X axis.
- the face gesture detection program 10 can also be partitioned into one or more modules that are stored in the memory 12 and executed by the processor 13 to complete the application.
- a module as referred to in this application refers to a series of computer program instructions that are capable of performing a particular function.
- FIG. 2 it is a functional block diagram of the face pose detecting program 10 of Fig. 1.
- the face gesture detection program 10 can be divided into an acquisition module 110, an extraction module 120, and a calculation module 130.
- the obtaining module 110 is configured to acquire a real-time image captured by the camera 11 and extract a real-time facial image from the real-time image by using a face recognition algorithm.
- the camera 11 captures a real-time image
- the camera 11 transmits the real-time image to the processor 13.
- the acquisition module 110 extracts the real-time using the face recognition algorithm. Face image.
- the face recognition algorithm for extracting the real-time facial image from the real-time image may be a geometric feature-based method, a local feature analysis method, a feature face method, an elastic model-based method, a neural network method, or the like.
- the extracting module 120 is configured to extract the facial feature points and the key feature points of the real-time facial image.
- the extraction module 120 extracts 68 human face feature points P1-P68 in the real-time facial image by the dlib face feature point detection algorithm, as shown in FIG. Further, five key feature points are extracted from the 68 facial feature points: a first feature point P1 near the right ear, a second feature point P3 near the right ear, and a third feature near the left ear. Point P15, near the fourth feature point P17 above the left ear, and the fifth feature point P31 at the tip of the nose.
- the calculation module 130 is configured to calculate a face rotation direction and an angle in the real-time face image according to coordinate values of five key feature points in the real-time face image.
- the calculation module 130 is configured to:
- the computing device 1 proposed in this embodiment extracts a real-time facial image from a real-time image, extracts a facial feature point in the real-time facial image by using a dlib facial feature point detection algorithm, and further extracts a key feature point, and then extracts a key feature point, and then The face rotation direction and angle in the face image are calculated according to the coordinate values of the key feature points, realizing the real-time detection of the face pose.
- the present application also provides a face gesture detection method.
- FIG. 4 it is a flowchart of a preferred embodiment of the applicant's face gesture detection method.
- the method can be performed by a computing device, which can be implemented by software and/or hardware.
- the face gesture detection method includes:
- the extraction module 120 extracts N facial feature points from the facial image by using a face detection algorithm.
- the camera 11 captures a real-time image and transmits the real-time image to the processor 13.
- the processor 13 extracts a real-time facial image by using a face recognition algorithm, and then acquires N through a face detection algorithm.
- personal face feature points extracting the real image from the real image
- the face recognition algorithm of the face image may be a geometric feature based method, a local feature analysis method, a feature face method, an elastic model based method, a neural network method, or the like.
- the face detection algorithm for extracting facial feature points from the face image is a dlib face feature point detection algorithm.
- step S20 the extraction module 120 extracts key feature points in the feature points of the face image.
- the extraction module 120 further extracts a first feature point P1 near the right ear, a second feature point P3 near the right ear, a third feature point P15 near the left ear, and a fourth feature point P17 near the left ear.
- the nose tip feature point is extracted as the fifth feature point P31.
- step S30 the calculation module 130 calculates a rotation angle of the face in the face image about the X axis.
- the fifth feature point P31 and the first feature point P1 form a first vector
- the fifth feature point P31 and the fourth feature point P17 form a second vector
- the first vector and the second vector are calculated.
- a first angle determining a rotation angle of the face in the face image about the X axis according to the change of the first angle and the initial angle;
- step S40 the calculation module 130 calculates a rotation angle of the face in the face image around the Y axis.
- the first feature point P1 and the fourth feature point P17 form a third vector
- a line L1 parallel to the X axis is extracted from the fourth feature point P17, and the third vector and the line L1 are calculated.
- the second angle determines the rotation angle of the face in the face image about the Y axis according to the second angle.
- step S50 the calculation module 130 calculates a rotation direction of the face in the face image about the Z axis.
- the distance between the second feature point P3 and the fifth feature point P31 is recorded as a first distance
- the distance between the third feature point P15 and the fifth feature point P31 is recorded as a second distance
- the first calculation is performed.
- the ratio of the distance to the second distance determines the direction of rotation of the face about the Z axis based on the ratio of the first distance to the second distance.
- the face pose detection method proposed in this embodiment uses the dlib face feature point detection algorithm to acquire the face feature points in the real-time face image, and further extracts the key feature points, and calculates the real-time according to the coordinate values of the key feature points.
- the face rotation direction and angle in the face image realize real-time detection of the face pose.
- the embodiment of the present application further provides a computer readable storage medium, where the computer readable storage medium includes a face gesture detection program, and when the face gesture detection program is executed by the processor, the following operations are implemented:
- a key feature point extraction step extracting, from the N facial feature points, a first feature point P1 near the right ear, a second feature point P3 near the right ear, and a part near the left ear a third feature point P15, a fourth feature point P17 near the left ear, and extracting a nose point feature point as a fifth feature point P31;
- a first calculating step forming the fifth feature point P31 and the first feature point P1 into a first vector, and the fifth feature point P31 and the fourth feature point P17 form a second vector (refer to FIG. 5), and calculating the first The first angle between the vector and the second vector, and the face map is determined according to the change of the first angle and the initial angle The angle of rotation of the human face around the X axis;
- a second calculating step forming the first feature point P1 and the fourth feature point P17 into a third vector, and extracting a line L1 parallel to the X axis from the fourth feature point P17 (refer to FIG. 6), and calculating the third a second angle between the vector and the straight line L1, and determining, according to the second angle, a rotation angle of the face in the face image about the Y axis;
- a third calculating step the distance between the second feature point P3 and the fifth feature point P31 is recorded as a first distance, and the distance between the third feature point P15 and the fifth feature point P31 is recorded as a second distance (refer to FIG. 7
- the ratio of the first distance to the second distance is calculated, and the rotation direction of the face around the Z axis in the face image is determined according to the ratio of the first distance to the second distance.
- the first calculating step includes:
- the first angle is greater than 120°, it indicates that the face in the face image rotates around the X axis, and the rotation angle is the difference between the first angle and the initial angle;
- the first angle is less than 120°, it indicates that the face in the face image is rotated downward about the X axis, and the rotation angle is the difference between the initial angle and the first angle;
- the first angle is equal to 120°, it indicates that the face in the face image is in an initial position with respect to the X axis, that is, not raised or bowed.
- the second calculating step includes:
- the second angle When the second angle is 0°, it indicates that the face in the face image is in an initial position with respect to the Y axis, that is, the head is not left or right;
- the third calculating step includes:
- the ratio of the first distance to the second distance is less than 1, it indicates that the face in the face image is rotated right around the Z axis.
- a disk including a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the methods described in the various embodiments of the present application.
- a terminal device which may be a mobile phone, a computer, a server, or a network device, etc.
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Abstract
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Claims (20)
- 一种人脸姿态检测方法,其特征在于,该方法包括:人脸特征点提取步骤:通过人脸检测算法从人脸图像中提取N个人脸特征点;关键特征点提取步骤:从所述N个人脸特征点中提取脸部轮廓特征点中靠近右耳上方的第一特征点、靠近右耳下方的第二特征点、靠近左耳下方的第三特征点、靠近左耳上方的第四特征点,并提取鼻尖特征点为第五特征点;第一计算步骤:将所述第五特征点与第一特征点组成第一向量,第五特征点与第四特征点组成第二向量,计算第一向量与第二向量的第一夹角,根据第一夹角与初始夹角的变化判断所述人脸图像中人脸绕X轴的旋转角度;第二计算步骤:将所述第一特征点与第四特征点组成第三向量,从第四特征点引出一条与X轴平行的直线L1,计算第三向量与直线L1的第二夹角,根据该第二夹角判断所述人脸图像中人脸绕Y轴的旋转角度;第三计算步骤:将所述第二特征点与第五特征点的距离记为第一距离,第三特征点与第五特征点的距离记为第二距离,计算第一距离与第二距离的比值,根据第一距离与第二距离的比值判断所述人脸图像中人脸绕Z轴的旋转方向。
- 根据权利要求1所述的人脸姿态检测方法,其特征在于,所述人脸检测算法为dlib人脸特征点检测算法。
- 根据权利要求1所述的人脸姿态检测方法,其特征在于,所述N个人脸特征点包括脸部轮廓特征点、眉部特征点、鼻翼特征点、鼻孔特征点、眼部特征点和唇部特征点。
- 根据权利要求1所述的人脸姿态检测方法,其特征在于,所述脸部轮廓特征点中,靠近右耳上方的第一特征点与靠近左耳上方的第四特征点对称,靠近右耳下方的第二特征点与靠近左耳下方的第三特征点对称。
- 根据权利要求1所述的人脸姿态检测方法,其特征在于,所述第一计算步骤包括:计算人脸在初始位置时所述第一向量与第二向量的初始夹角;当所述第一夹角大于初始夹角时,表明所述人脸图像中人脸绕X轴向上旋转,旋转角度为第一夹角与初始夹角之差;当所述第一夹角小于初始夹角时,表明所述人脸图像中人脸绕X轴向下旋转,旋转角度为初始夹角与第一夹角之差;当所述第一夹角等于初始夹角时,表明所述人脸图像中人脸相对于X轴处于初始位置。
- 根据权利要求1所述的人脸姿态检测方法,其特征在于,所述第二计算步骤包括:当所述第二夹角为0°时,表明所述人脸图像中人脸相对于Y轴处于初始位置;当所述第二夹角不为0°且所述第一特征点在所述直线L1上方时,表明所述人脸图像中人脸绕Y轴向左旋转,旋转角度为第二夹角的角度值;当所述第二夹角不为0°且所述第一特征点在所述直线L1下方时,表明所述人脸图像中人脸绕Y轴向右旋转,旋转角度为第二夹角的角度值。
- 根据权利要求1所述的人脸姿态检测方法,其特征在于,所述第三计算步骤包括:当所述第一距离与第二距离的比值等于1时,表明所述人脸图像中人脸相对于Z轴处于初始位置;当所述第一距离与第二距离的比值大于1时,表明所述人脸图像中人脸绕Z轴向左旋转;当所述第一距离与第二距离的比值小于1时,表明所述人脸图像中人脸绕Z轴向右旋转。
- 一种计算装置,包括摄像装置、存储器和处理器,其特征在于,所述存储器中包括人脸姿态检测程序,所述人脸姿态检测程序被所述处理器执行时实现如下步骤:人脸特征点提取步骤:通过人脸检测算法从人脸图像中提取N个人脸特征点;关键特征点提取步骤:从所述N个人脸特征点中提取脸部轮廓特征点中靠近右耳上方的第一特征点、靠近右耳下方的第二特征点、靠近左耳下方的第三特征点、靠近左耳上方的第四特征点,并提取鼻尖特征点为第五特征点;第一计算步骤:将所述第五特征点与第一特征点组成第一向量,第五特征点与第四特征点组成第二向量,计算第一向量与第二向量的第一夹角,根据第一夹角与初始夹角的变化判断所述人脸图像中人脸绕X轴的旋转角度;第二计算步骤:将所述第一特征点与第四特征点组成第三向量,从第四特征点引出一条与X轴平行的直线L1,计算第三向量与直线L1的第二夹角,根据该第二夹角判断所述人脸图像中人脸绕Y轴的旋转角度;第三计算步骤:将所述第二特征点与第五特征点的距离记为第一距离,第三特征点与第五特征点的距离记为第二距离,计算第一距离与第二距离的比值,根据第一距离与第二距离的比值判断所述人脸图像中人脸绕Z轴的旋转方向。
- 根据权利要求8所述的计算装置,其特征在于,所述人脸检测算法为 dlib人脸特征点检测算法。
- 根据权利要求8所述的计算装置,其特征在于,所述N个人脸特征点包括脸部轮廓特征点、眉部特征点、鼻翼特征点、鼻孔特征点、眼部特征点和唇部特征点。
- 根据权利要求8所述的计算装置,其特征在于,所述脸部轮廓特征点中,靠近右耳上方的第一特征点与靠近左耳上方的第四特征点对称,靠近右耳下方的第二特征点与靠近左耳下方的第三特征点对称。
- 根据权利要求8所述的计算装置,其特征在于,所述第一计算步骤包括:计算人脸在初始位置时所述第一向量与第二向量的初始夹角;当所述第一夹角大于初始夹角时,表明所述人脸图像中人脸绕X轴向上旋转,旋转角度为第一夹角与初始夹角之差;当所述第一夹角小于初始夹角时,表明所述人脸图像中人脸绕X轴向下旋转,旋转角度为初始夹角与第一夹角之差;当所述第一夹角等于初始夹角时,表明所述人脸图像中人脸相对于X轴处于初始位置。
- 根据权利要求8所述的计算装置,其特征在于,所述第二计算步骤包括:当所述第二夹角为0°时,表明所述人脸图像中人脸相对于Y轴处于初始位置;当所述第二夹角不为0°且所述第一特征点在所述直线L1上方时,表明所述人脸图像中人脸绕Y轴向左旋转,旋转角度为第二夹角的角度值;当所述第二夹角不为0°且所述第一特征点在所述直线L1下方时,表明所述人脸图像中人脸绕Y轴向右旋转,旋转角度为第二夹角的角度值。
- 根据权利要求8所述的计算装置,其特征在于,所述第三计算步骤包括:当所述第一距离与第二距离的比值等于1时,表明所述人脸图像中人脸相对于Z轴处于初始位置;当所述第一距离与第二距离的比值大于1时,表明所述人脸图像中人脸绕Z轴向左旋转;当所述第一距离与第二距离的比值小于1时,表明所述人脸图像中人脸绕Z轴向右旋转。
- 一种计算机可读存储介质,其特征在于,所述计算机可读存储介质 中包括人脸姿态检测程序,所述人脸姿态检测程序被所述处理器执行时,实现如下步骤:人脸特征点提取步骤:通过人脸检测算法从人脸图像中提取N个人脸特征点;关键特征点提取步骤:从所述N个人脸特征点中提取脸部轮廓特征点中靠近右耳上方的第一特征点、靠近右耳下方的第二特征点、靠近左耳下方的第三特征点、靠近左耳上方的第四特征点,并提取鼻尖特征点为第五特征点;第一计算步骤:将所述第五特征点与第一特征点组成第一向量,第五特征点与第四特征点组成第二向量,计算第一向量与第二向量的第一夹角,根据第一夹角与初始夹角的变化判断所述人脸图像中人脸绕X轴的旋转角度;第二计算步骤:将所述第一特征点与第四特征点组成第三向量,从第四特征点引出一条与X轴平行的直线L1,计算第三向量与直线L1的第二夹角,根据该第二夹角判断所述人脸图像中人脸绕Y轴的旋转角度;第三计算步骤:将所述第二特征点与第五特征点的距离记为第一距离,第三特征点与第五特征点的距离记为第二距离,计算第一距离与第二距离的比值,根据第一距离与第二距离的比值判断所述人脸图像中人脸绕Z轴的旋转方向。
- 根据权利要求15所述的计算机可读存储介质,其特征在于,所述人脸检测算法为dlib人脸特征点检测算法;所述N个人脸特征点包括脸部轮廓特征点、眉部特征点、鼻翼特征点、鼻孔特征点、眼部特征点和唇部特征点。
- 根据权利要求15所述的计算机可读存储介质,其特征在于,所述脸部轮廓特征点中,靠近右耳上方的第一特征点与靠近左耳上方的第四特征点对称,靠近右耳下方的第二特征点与靠近左耳下方的第三特征点对称。
- 根据权利要求15所述的计算机可读存储介质,其特征在于,所述第一计算步骤包括:计算人脸在初始位置时所述第一向量与第二向量的初始夹角;当所述第一夹角大于初始夹角时,表明所述人脸图像中人脸绕X轴向上旋转,旋转角度为第一夹角与初始夹角之差;当所述第一夹角小于初始夹角时,表明所述人脸图像中人脸绕X轴向下旋转,旋转角度为初始夹角与第一夹角之差;当所述第一夹角等于初始夹角时,表明所述人脸图像中人脸相对于X轴处于初始位置。
- 根据权利要求15所述的计算机可读存储介质,其特征在于,所述第二计算步骤包括:当所述第二夹角为0°时,表明所述人脸图像中人脸相对于Y轴处于初始位置;当所述第二夹角不为0°且所述第一特征点在所述直线L1上方时,表明所述人脸图像中人脸绕Y轴向左旋转,旋转角度为第二夹角的角度值;当所述第二夹角不为0°且所述第一特征点在所述直线L1下方时,表明所述人脸图像中人脸绕Y轴向右旋转,旋转角度为第二夹角的角度值。
- 根据权利要求15所述的计算机可读存储介质,其特征在于,所述第三计算步骤包括:当所述第一距离与第二距离的比值等于1时,表明所述人脸图像中人脸相对于Z轴处于初始位置;当所述第一距离与第二距离的比值大于1时,表明所述人脸图像中人脸绕Z轴向左旋转;当所述第一距离与第二距离的比值小于1时,表明所述人脸图像中人脸绕Z轴向右旋转。
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Publication number | Priority date | Publication date | Assignee | Title |
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CN113642368A (zh) * | 2020-05-11 | 2021-11-12 | 杭州海康威视数字技术股份有限公司 | 人脸姿态的确定方法、装置、设备及存储介质 |
Families Citing this family (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10867161B2 (en) * | 2017-09-06 | 2020-12-15 | Pixart Imaging Inc. | Auxiliary filtering device for face recognition and starting method for electronic device |
CN108549487A (zh) * | 2018-04-23 | 2018-09-18 | 网易(杭州)网络有限公司 | 虚拟现实交互方法与装置 |
AU2018420579A1 (en) | 2018-04-25 | 2020-07-02 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for shaking action recognition based on facial feature points |
CN109086727B (zh) * | 2018-08-10 | 2021-04-30 | 北京奇艺世纪科技有限公司 | 一种确定人体头部的运动角度的方法、装置及电子设备 |
CN109522863B (zh) * | 2018-11-28 | 2020-11-27 | 北京达佳互联信息技术有限公司 | 耳部关键点检测方法、装置及存储介质 |
CN110222651A (zh) * | 2019-06-10 | 2019-09-10 | Oppo广东移动通信有限公司 | 一种人脸姿态检测方法、装置、终端设备及可读存储介质 |
CN112307817B (zh) * | 2019-07-29 | 2024-03-19 | 中国移动通信集团浙江有限公司 | 人脸活体检测方法、装置、计算设备及计算机存储介质 |
CN111104846B (zh) * | 2019-10-16 | 2022-08-30 | 平安科技(深圳)有限公司 | 数据检测方法、装置、计算机设备和存储介质 |
JPWO2022264268A1 (zh) * | 2021-06-15 | 2022-12-22 | ||
JP7481398B2 (ja) | 2022-07-04 | 2024-05-10 | ソフトバンク株式会社 | 判定装置、プログラム、及び判定方法 |
KR102538783B1 (ko) * | 2022-09-27 | 2023-06-01 | 주식회사 미러로이드 | 가상 헤어 스타일링 체험을 위한 민머리 인물 이미지 생성 장치 및 방법 |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20040062424A1 (en) * | 1999-11-03 | 2004-04-01 | Kent Ridge Digital Labs | Face direction estimation using a single gray-level image |
CN102054291A (zh) * | 2009-11-04 | 2011-05-11 | 厦门市美亚柏科信息股份有限公司 | 一种基于单幅人脸图像实现三维人脸重建的方法及其装置 |
CN103605965A (zh) * | 2013-11-25 | 2014-02-26 | 苏州大学 | 一种多姿态人脸识别方法和装置 |
KR20150127381A (ko) * | 2014-05-07 | 2015-11-17 | 포항공과대학교 산학협력단 | 얼굴 특징점 추출 방법 및 이를 수행하는 장치 |
CN105389570A (zh) * | 2015-11-19 | 2016-03-09 | 吴建忠 | 一种人脸角度判定方法及其系统 |
CN106991367A (zh) * | 2016-01-21 | 2017-07-28 | 腾讯科技(深圳)有限公司 | 确定人脸转动角度的方法和装置 |
Family Cites Families (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP3590321B2 (ja) * | 2000-03-28 | 2004-11-17 | 株式会社メディックエンジニアリング | 人物照合システム |
US6671391B1 (en) * | 2000-05-26 | 2003-12-30 | Microsoft Corp. | Pose-adaptive face detection system and process |
JP4204336B2 (ja) * | 2003-01-30 | 2009-01-07 | 富士通株式会社 | 顔の向き検出装置、顔の向き検出方法及びコンピュータプログラム |
JP4946730B2 (ja) * | 2007-08-27 | 2012-06-06 | ソニー株式会社 | 顔画像処理装置及び顔画像処理方法、並びにコンピュータ・プログラム |
JP5432532B2 (ja) * | 2008-01-22 | 2014-03-05 | 株式会社 資生堂 | 化粧方法、化粧シミュレーション装置、及び化粧シミュレーションプログラム |
EP2942980A1 (en) * | 2014-05-08 | 2015-11-11 | GN Store Nord A/S | Real-time control of an acoustic environment |
WO2015186519A1 (ja) * | 2014-06-06 | 2015-12-10 | シャープ株式会社 | 画像処理装置及び画像表示装置 |
JP5950486B1 (ja) * | 2015-04-01 | 2016-07-13 | みずほ情報総研株式会社 | 加齢化予測システム、加齢化予測方法及び加齢化予測プログラム |
US10330958B2 (en) * | 2015-04-10 | 2019-06-25 | Bespoke, Inc. | Systems and methods for creating eyewear with multi-focal lenses |
US20200367630A1 (en) * | 2017-12-07 | 2020-11-26 | Koninklijke Philips N.V. | A frame unit |
JP6981325B2 (ja) * | 2018-03-22 | 2021-12-15 | 株式会社Jvcケンウッド | 処理装置、処理方法、及びプログラム |
KR102537784B1 (ko) * | 2018-08-17 | 2023-05-30 | 삼성전자주식회사 | 전자 장치 및 그 제어 방법 |
CN109345253A (zh) * | 2018-09-04 | 2019-02-15 | 阿里巴巴集团控股有限公司 | 资源转移方法、装置及系统 |
WO2021062440A1 (en) * | 2019-09-24 | 2021-04-01 | Bespoke, Inc. d/b/a Topology Eyewear | Systems and methods for adjusting stock eyewear frames using a 3d scan of facial features |
-
2017
- 2017-08-17 CN CN201710707941.2A patent/CN107679446B/zh active Active
- 2017-10-31 US US16/632,371 patent/US11062124B2/en active Active
- 2017-10-31 WO PCT/CN2017/108803 patent/WO2019033576A1/zh active Application Filing
- 2017-10-31 SG SG11202001403XA patent/SG11202001403XA/en unknown
- 2017-10-31 JP JP2019556821A patent/JP6815707B2/ja active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20040062424A1 (en) * | 1999-11-03 | 2004-04-01 | Kent Ridge Digital Labs | Face direction estimation using a single gray-level image |
CN102054291A (zh) * | 2009-11-04 | 2011-05-11 | 厦门市美亚柏科信息股份有限公司 | 一种基于单幅人脸图像实现三维人脸重建的方法及其装置 |
CN103605965A (zh) * | 2013-11-25 | 2014-02-26 | 苏州大学 | 一种多姿态人脸识别方法和装置 |
KR20150127381A (ko) * | 2014-05-07 | 2015-11-17 | 포항공과대학교 산학협력단 | 얼굴 특징점 추출 방법 및 이를 수행하는 장치 |
CN105389570A (zh) * | 2015-11-19 | 2016-03-09 | 吴建忠 | 一种人脸角度判定方法及其系统 |
CN106991367A (zh) * | 2016-01-21 | 2017-07-28 | 腾讯科技(深圳)有限公司 | 确定人脸转动角度的方法和装置 |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113642368A (zh) * | 2020-05-11 | 2021-11-12 | 杭州海康威视数字技术股份有限公司 | 人脸姿态的确定方法、装置、设备及存储介质 |
CN113642368B (zh) * | 2020-05-11 | 2023-08-18 | 杭州海康威视数字技术股份有限公司 | 人脸姿态的确定方法、装置、设备及存储介质 |
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