CN108162915A - Vehicle-mounted middle control personalized configuration system based on recognition of face - Google Patents
Vehicle-mounted middle control personalized configuration system based on recognition of face Download PDFInfo
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- CN108162915A CN108162915A CN201711420653.5A CN201711420653A CN108162915A CN 108162915 A CN108162915 A CN 108162915A CN 201711420653 A CN201711420653 A CN 201711420653A CN 108162915 A CN108162915 A CN 108162915A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R25/00—Fittings or systems for preventing or indicating unauthorised use or theft of vehicles
- B60R25/20—Means to switch the anti-theft system on or off
- B60R25/25—Means to switch the anti-theft system on or off using biometry
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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
- G06V40/168—Feature extraction; Face representation
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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
- G06V40/172—Classification, e.g. identification
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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
- G06V40/174—Facial expression recognition
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Abstract
The present invention relates to face recognition technologies.The present invention is to solve existing recognition of face, recognition effect is not fully up to expectations inside the vehicle, lead to not realize identification driver identity, the problem of so as to which individual cultivation can not be carried out for it, a kind of vehicle-mounted middle control personalized configuration system based on recognition of face is provided, technical solution can be summarized as：In vehicle-mounted middle control personalized configuration system based on recognition of face, image information collecting unit is connect with cloud platform, and image information collecting unit is used for the human face image information using active near-infrared image acquisition mode acquisition driver, and is uploaded to cloud platform；Cloud platform is stored and is pre-processed to obtained human face image information, extract its face characteristic, big data analysis matching and identification are carried out by the face characteristic of extraction, obtain the individual cultivation scheme of corresponding driver, and sends it to corresponding vehicle-mounted middle control terminal and performs.The invention has the advantages that user experience is promoted, the control suitable for vehicle-mounted.
Description
Technical field
The present invention relates to face recognition technology, the technology of recognition of face more particularly in car networking.
Background technology
Although with big data and artificial intelligence, the continuous development of biological identification technology, especially in car networking industry field
Application of constantly bringing forth new ideas, but vehicle-mounted middle control terminal can't realize the personalized customization service application to vary with each individual.To pass through
Face recognition technology carries out driver identity identification, and formulate driver personalityization by big data analysis based on this
Application configuration, then due to current recognition of face, recognition effect is not fully up to expectations inside the vehicle, leads to not realize, reason exists
In：Traditional face recognition technology is mainly based upon the recognition of face of visible images, the defects of being difficult to overcome, in ring
When border illumination changes, especially its recognition effect can drastically decline in interior environment, can not meet real system
Demand, and the scheme for solving lighting issues has 3-D view recognition of face and thermal imaging recognition of face, but both technologies at present
Also remote immature, recognition effect is not fully up to expectations.
Invention content
Due to current recognition of face, recognition effect is not fully up to expectations inside the vehicle the invention aims to solving, and causes
It can not realize identification driver identity, thus the problem of individual cultivation can not be carried out for it, provide a kind of based on face knowledge
Other vehicle-mounted middle control personalized configuration system.
The present invention solves its technical problem, the technical solution adopted is that, the vehicle-mounted middle control personalization based on recognition of face is matched
Put system, which is characterized in that including image information collecting unit and cloud platform, described image information acquisition unit connects with cloud platform
It connects,
Described image information acquisition unit is used for the face figure using active near-infrared image acquisition mode acquisition driver
As information, and it is uploaded to cloud platform；
The cloud platform is stored and is pre-processed to obtained human face image information, its face characteristic is extracted, by carrying
The face characteristic taken carries out big data analysis matching and identification, obtains the individual cultivation scheme of corresponding driver, and sent out
Corresponding vehicle-mounted middle control terminal is given to perform.
Specifically, the human face image information using active near-infrared image acquisition mode acquisition driver, and upload
Refer to cloud platform：When driver is in the range of image information collecting unit photographs, image information collecting unit is using actively
Near-infrared image acquisition mode is searched for and shoots the human face image information of driver automatically, and it is uploaded to cloud platform in real time.
Further, the human face image information is still image or dynamic image；The still image or dynamic image
For driver's different location and/or the still image or dynamic image of different expressions.
Specifically, the pretreatment refers to：Light compensation, greyscale transformation, histogram equalization are carried out to human face image information
Change, normalization, geometric correction, filtering and sharpening.
Further, the face characteristic includes visual signature, pixels statistics feature, facial image transformation coefficient feature
And facial image algebraic characteristic.
Specifically, described its face characteristic of extraction, big data analysis matching and identification are carried out by the face characteristic of extraction
Refer to：A threshold value is preset, extracts face characteristic, the face characteristic extracted and the facial image identification model to prestore are carried out
Similarity mode identifies, if similarity is more than or equal to preset threshold value, exports the matching result, obtains corresponding driver's letter
Breath.
Further, the face characteristic extracted and the facial image to prestore are identified mould by the extraction face characteristic
Type is carried out in similarity mode identification, using the face recognition algorithms for illumination variation.
Specifically, the face recognition algorithms for illumination variation include the following steps：
Step 1, using IAS algorithm process original image I, obtain ILAS, specific formula is：
w(t)(x, y)=g (d(t)(x,y))
Wherein, N(t)(x, y) is standardizing factor；w(t)(x, y) is the coefficient of template；G represents propagation function, to be non-negative and
The function of monotone decreasing, g (d(t)(x, y)) with d(t)The increase of (x, y) and be intended to 0, the property of propagation function determines calculation
The smooth effect of method；D (t) (x, y) represents the variation degree of each pixel, L(t+1)It is the illumination point that the t+1 times iterative estimate goes out
Amount, (x, y) be pixel coordinate position；
Step 2, using LCE algorithms to original image I processing, obtain ILCE, specific formula is：
Wherein, I (m, n) is gray value of the image in (m, n) coordinate, and Y (m, n) is one carried out after LCE algorithmic transformations
Image value；
Step 3, the standard deviation sd1 and sd2 for calculating ILAS and ILCE respectively, calculation formula are：
Wherein, μ is the mean value of the pixel of image, and σ is standard deviation, standard deviation by first calculating mean μ, then calculated,
Sd1 is the standard deviation sigma of ILAS, and sd2 is the standard deviation sigma of ILCE；
Weighted Fusion coefficient ω 1 is calculated and ω 2, calculation formula are：
ω1=sd1/(sd1+sd2)
ω2=sd2/(sd1+sd2)
Handling result is obtained by blending algorithm；
Step 4 carries out Classification and Identification to handling result with rarefaction representation.
Further, the individual cultivation scheme for obtaining corresponding driver refers to：Obtain corresponding driver information
Afterwards, big data analysis classification, the individual character recommended are carried out according to the setting of the application operating of the driver and usage history data
Change allocation plan.
The invention has the advantages that in the present invention program, pass through the above-mentioned vehicle-mounted middle control individual character based on recognition of face
Change configuration system, the problem of using active near-infrared image acquisition mode, avoid ambient lighting, can realize driver's
Configuration is controlled in personalization, promotes user experience.
Description of the drawings
Fig. 1 is the system block diagram of the vehicle-mounted middle control personalized configuration system based on recognition of face in the embodiment of the present invention.
Specific embodiment
With reference to embodiment and attached drawing, detailed description of the present invention technical solution.
Vehicle-mounted middle control personalized configuration system of the present invention based on recognition of face, including image information collecting unit and
Cloud platform, wherein, image information collecting unit is connect with cloud platform, and here, image information collecting unit is used for using actively near
Infrared image acquisition mode acquires the human face image information of driver, and is uploaded to cloud platform；Cloud platform is to obtained face figure
As information is stored and is pre-processed, extract its face characteristic, by the face characteristic of extraction carry out big data analysis matching with
Identification obtains the individual cultivation scheme of corresponding driver, and sends it to corresponding vehicle-mounted middle control terminal and perform.
Embodiment
The vehicle-mounted middle control personalized configuration system based on recognition of face of the embodiment of the present invention, system block diagram referring to Fig. 1,
Including image information collecting unit and cloud platform, wherein, image information collecting unit is connect with cloud platform.
Here, image information collecting unit is used for the face figure using active near-infrared image acquisition mode acquisition driver
As information, and it is uploaded to cloud platform.Using the human face image information of active near-infrared image acquisition mode acquisition driver, and on
Reaching cloud platform can be：When driver is in the range of image information collecting unit photographs, image information collecting unit uses
Active near-infrared image acquisition mode is searched for and shoots the human face image information of driver, and it is uploaded to cloud in real time and is put down automatically
Platform.Human face image information can be still image or dynamic image, specially driver's different location and/or different expressions
Still image or dynamic image.Multiple light courcess face recognition technology use intensity based on active near-infrared image is higher than ambient light
Positive near infrared light source imaging, coordinate the optical filter of corresponding wave band, the unrelated facial image of environment, face figure can be obtained
As can only be monotonically changed with the distance change of people and camera.
Cloud platform is stored and is pre-processed to obtained human face image information, is extracted its face characteristic, is passed through extraction
Face characteristic carries out big data analysis matching and identification, obtains the individual cultivation scheme of corresponding driver, and send it to
Corresponding vehicle-mounted middle control terminal performs.
In this example, pretreatment is based on Face datection as a result, being handled image and finally serving feature extraction
Process.The original image that system obtains by various conditions due to being limited and random disturbances, it is impossible to directly use, it is necessary to scheme
As the early stage of processing carries out the image preprocessings such as gray correction, noise filtering, therefore pretreatment can include to it：To people
Face image information carries out light compensation, greyscale transformation, histogram equalization, normalization, geometric correction, filtering and sharpening etc., this
For the prior art, no further details here.
And face characteristic may include visual signature, pixels statistics feature, facial image transformation coefficient feature and facial image
Algebraic characteristic etc., face characteristic extract what is carried out aiming at certain features of face.Wherein Knowledge based engineering characterizing method master
If according to the shape description of human face and they the distance between characteristic contribute to the characteristic of face classification to obtain
According to including Euclidean distance, curvature and angle between characteristic point etc..Face is locally made of eyes, nose, mouth, chin etc., to this
A little local and structural relation between them geometric description, can be as the important feature of identification face, this is the prior art, herein
No longer it is described in detail.
In this example, its face characteristic is extracted, carrying out big data analysis matching and identification by the face characteristic of extraction can be with
For：A threshold value is preset, extracts face characteristic, the face characteristic extracted is subjected to phase with the facial image identification model to prestore
Like degree match cognization, if similarity is more than or equal to preset threshold value, the matching result is exported, obtains corresponding driver's letter
Breath.
Face characteristic is extracted, the face characteristic extracted and the facial image identification model to prestore are subjected to similarity mode
In identification, the face recognition algorithms for illumination variation can be used.
Then following steps are preferably comprised for the face recognition algorithms of illumination variation：
Step 1, using IAS algorithm process original image I, obtain ILAS, specific formula is：
w(t)(x, y)=g (d(t)(x,y))
Wherein, N(t)(x, y) is standardizing factor；w(t)(x, y) is the coefficient of template；G represents propagation function, to be non-negative and
The function of monotone decreasing, g (d(t)(x, y)) with d(t)The increase of (x, y) and be intended to 0, the property of propagation function determines calculation
The smooth effect of method；D (t) (x, y) represents the variation degree of each pixel, L(t+1)It is the illumination point that the t+1 times iterative estimate goes out
Amount, (x, y) be pixel coordinate position, image is a matrix data, some can be uniquely determined by (x, y) coordinate
Pixel；
Step 2, using LCE algorithms to original image I processing, obtain ILCE, specific formula is：
Wherein, I (m, n) is gray value of the image in (m, n) coordinate, and Y (m, n) is one carried out after LCE algorithmic transformations
Image value；
Step 3, the standard deviation sd1 and sd2 for calculating ILAS and ILCE respectively, calculation formula are：
Wherein, μ is the mean value of the pixel of image, and σ is standard deviation, standard deviation by first calculating mean μ, then calculated,
I.e. following formula is the basis of formula above, and sd1 is the standard deviation sigma of ILAS, and sd2 is the standard deviation sigma of ILCE, the entirety of algorithm
It is that thinking is, source images is carried out to the algorithmic transformation of step 1 and step 2 respectively, then calculates the standard of ILAS and ILCE respectively
Poor sd1 and sd2 obtains Weighted Fusion coefficient ω 1 and ω 2, and the unrelated facial image of final illumination is obtained by blending algorithm；
Weighted Fusion coefficient ω 1 is calculated and ω 2, calculation formula are：
ω1=sd1/(sd1+sd2)
ω2=sd2/(sd1+sd2)
Handling result is obtained by blending algorithm；
Step 4 carries out Classification and Identification to handling result with rarefaction representation.
Here, the individual cultivation scheme for obtaining corresponding driver is preferably：After obtaining corresponding driver information, according to this
The application operating setting of driver and usage history data carry out big data analysis classification, the individual cultivation side recommended
Case.
Claims (9)
1. the vehicle-mounted middle control personalized configuration system based on recognition of face, which is characterized in that including image information collecting unit and
Cloud platform, described image information acquisition unit are connect with cloud platform,
Described image information acquisition unit is used for the facial image letter using active near-infrared image acquisition mode acquisition driver
Breath, and it is uploaded to cloud platform；
The cloud platform is stored and is pre-processed to obtained human face image information, is extracted its face characteristic, is passed through extraction
Face characteristic carries out big data analysis matching and identification, obtains the individual cultivation scheme of corresponding driver, and send it to
Corresponding vehicle-mounted middle control terminal performs.
2. the vehicle-mounted middle control personalized configuration system based on recognition of face as described in claim 1, which is characterized in that described to adopt
The human face image information of driver is acquired with active near-infrared image acquisition mode, and is uploaded to cloud platform and refers to：Work as driver
When in the range of image information collecting unit photographs, image information collecting unit is automatic using active near-infrared image acquisition mode
The human face image information of driver is searched for and shot, and it is uploaded to cloud platform in real time.
3. the vehicle-mounted middle control personalized configuration system based on recognition of face as described in claim 1, which is characterized in that the people
Face image information is still image or dynamic image；The still image or dynamic image for driver's different location and/or
The still image or dynamic image of different expressions.
4. the vehicle-mounted middle control personalized configuration system based on recognition of face as described in claim 1, which is characterized in that described pre-
Processing refers to：Light compensation, greyscale transformation, histogram equalization, normalization, geometric correction, filter are carried out to human face image information
Involve sharpening.
5. the vehicle-mounted middle control personalized configuration system based on recognition of face as described in claim 1, which is characterized in that the people
Face feature includes visual signature, pixels statistics feature, facial image transformation coefficient feature and facial image algebraic characteristic.
6. the vehicle-mounted middle control personalized configuration system based on recognition of face as described in claim 1, which is characterized in that described to carry
Its face characteristic is taken, carry out big data analysis matching by the face characteristic of extraction refers to identification：Preset a threshold value, extraction
The face characteristic extracted and the facial image identification model to prestore are carried out similarity mode identification, if similar by face characteristic
Degree is more than or equal to preset threshold value, then exports the matching result, obtain corresponding driver information.
7. the vehicle-mounted middle control personalized configuration system based on recognition of face as described in claim 1, which is characterized in that described to carry
Face characteristic is taken, the face characteristic extracted and the facial image identification model to prestore are carried out in similarity mode identification, adopted
With the face recognition algorithms for illumination variation.
8. the vehicle-mounted middle control personalized configuration system based on recognition of face as claimed in claim 7, which is characterized in that the needle
The face recognition algorithms of illumination variation are included the following steps：
Step 1, using IAS algorithm process original image I, obtain ILAS, specific formula is：
Wherein, N(t)(x, y) is standardizing factor；w(t)(x, y) is the coefficient of template；G represents propagation function, is non-negative and dull
The function to successively decrease, g (d(t)(x, y)) with d(t)The increase of (x, y) and be intended to 0, the property of propagation function determines algorithm
Smooth effect；D (t) (x, y) represents the variation degree of each pixel, L(t+1)It is the illumination component that the t+1 times iterative estimate goes out,
(x, y) is the coordinate position of pixel；
Step 2, using LCE algorithms to original image I processing, obtain ILCE, specific formula is：
Wherein, I (m, n) is gray value of the image in (m, n) coordinate, and Y (m, n) is the image carried out after LCE algorithmic transformations
Value；
Step 3, the standard deviation sd1 and sd2 for calculating ILAS and ILCE respectively, calculation formula are：
Wherein, μ is the mean value of the pixel of image, and σ is standard deviation, and by first calculating mean μ, then calculated, sd1 is standard deviation
The standard deviation sigma of ILAS, sd2 are the standard deviation sigmas of ILCE；
Weighted Fusion coefficient ω 1 is calculated and ω 2, calculation formula are：
ω1=sd1/(sd1+sd2)
ω2=sd2/(sd1+sd2)
Handling result is obtained by blending algorithm；
Step 4 carries out Classification and Identification to handling result with rarefaction representation.
9. the vehicle-mounted middle control individual cultivation based on recognition of face as described in claims 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8
System, which is characterized in that the individual cultivation scheme for obtaining corresponding driver refers to：After obtaining corresponding driver information,
Big data analysis classification is carried out according to the setting of the application operating of the driver and usage history data, the personalization recommended is matched
Put scheme.
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| CN109508685A (en) * | 2018-11-23 | 2019-03-22 | 赵雷 | Power communication dispatching method based on face recognition technology |
| CN109635543A (en) * | 2018-12-05 | 2019-04-16 | 四川长虹电器股份有限公司 | Automobile shows information individual character matching system and method |
| CN109703571A (en) * | 2018-12-24 | 2019-05-03 | 北京长城华冠汽车技术开发有限公司 | A kind of vehicle entertainment system login system and login method based on recognition of face |
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| CN110061984A (en) * | 2019-04-12 | 2019-07-26 | 广州小鹏汽车科技有限公司 | Account switching method, onboard system and the vehicle of onboard system |
| CN112101186A (en) * | 2020-09-11 | 2020-12-18 | 广州小鹏自动驾驶科技有限公司 | Device and method for identifying a vehicle driver and use thereof |
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| CN110015266A (en) * | 2019-03-21 | 2019-07-16 | 武汉格罗夫氢能汽车有限公司 | A kind of Vehicular intelligent is opened and Personalized service method |
| CN110061984A (en) * | 2019-04-12 | 2019-07-26 | 广州小鹏汽车科技有限公司 | Account switching method, onboard system and the vehicle of onboard system |
| CN110061984B (en) * | 2019-04-12 | 2022-03-18 | 广州小鹏汽车科技有限公司 | Account number switching method of vehicle-mounted system, vehicle-mounted system and vehicle |
| CN112101186A (en) * | 2020-09-11 | 2020-12-18 | 广州小鹏自动驾驶科技有限公司 | Device and method for identifying a vehicle driver and use thereof |
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Application publication date: 20180615 |