CN111122687B - Anti-terrorist security inspection method for explosives - Google Patents
Anti-terrorist security inspection method for explosives Download PDFInfo
- Publication number
- CN111122687B CN111122687B CN201911150122.8A CN201911150122A CN111122687B CN 111122687 B CN111122687 B CN 111122687B CN 201911150122 A CN201911150122 A CN 201911150122A CN 111122687 B CN111122687 B CN 111122687B
- Authority
- CN
- China
- Prior art keywords
- point
- face
- dimensional
- particles
- molecules
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000002360 explosive Substances 0.000 title claims abstract description 44
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000007689 inspection Methods 0.000 title claims abstract description 18
- 239000002245 particle Substances 0.000 claims abstract description 37
- 238000001514 detection method Methods 0.000 claims description 30
- 238000012360 testing method Methods 0.000 claims description 24
- 210000000744 eyelid Anatomy 0.000 claims description 14
- 239000013598 vector Substances 0.000 claims description 11
- 238000005070 sampling Methods 0.000 claims description 10
- 238000001871 ion mobility spectroscopy Methods 0.000 claims description 9
- 210000004709 eyebrow Anatomy 0.000 claims description 8
- 238000012795 verification Methods 0.000 claims description 6
- 238000010438 heat treatment Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 238000012937 correction Methods 0.000 claims description 4
- 238000009499 grossing Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 4
- 239000004065 semiconductor Substances 0.000 claims description 4
- 239000003153 chemical reaction reagent Substances 0.000 claims description 3
- 230000007246 mechanism Effects 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 3
- 238000012952 Resampling Methods 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 claims description 2
- 230000004069 differentiation Effects 0.000 claims description 2
- 239000000017 hydrogel Substances 0.000 claims description 2
- 238000000513 principal component analysis Methods 0.000 claims description 2
- 238000013139 quantization Methods 0.000 claims description 2
- 239000000758 substrate Substances 0.000 claims 1
- 230000001131 transforming effect Effects 0.000 claims 1
- 150000002500 ions Chemical class 0.000 description 9
- 230000008859 change Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000007613 environmental effect Effects 0.000 description 4
- 239000000203 mixture Substances 0.000 description 3
- 238000004611 spectroscopical analysis Methods 0.000 description 3
- 238000013135 deep learning Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000010419 fine particle Substances 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- SPSSULHKWOKEEL-UHFFFAOYSA-N 2,4,6-trinitrotoluene Chemical compound CC1=C([N+]([O-])=O)C=C([N+]([O-])=O)C=C1[N+]([O-])=O SPSSULHKWOKEEL-UHFFFAOYSA-N 0.000 description 1
- PAWQVTBBRAZDMG-UHFFFAOYSA-N 2-(3-bromo-2-fluorophenyl)acetic acid Chemical compound OC(=O)CC1=CC=CC(Br)=C1F PAWQVTBBRAZDMG-UHFFFAOYSA-N 0.000 description 1
- NOQGZXFMHARMLW-UHFFFAOYSA-N Daminozide Chemical compound CN(C)NC(=O)CCC(O)=O NOQGZXFMHARMLW-UHFFFAOYSA-N 0.000 description 1
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 1
- 238000001069 Raman spectroscopy Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000007664 blowing Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000012864 cross contamination Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 239000003350 kerosene Substances 0.000 description 1
- 238000004949 mass spectrometry Methods 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013618 particulate matter Substances 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 239000000015 trinitrotoluene Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/62—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- Multimedia (AREA)
- Educational Administration (AREA)
- Electrochemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Computer Security & Cryptography (AREA)
- Human Computer Interaction (AREA)
- Development Economics (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Collating Specific Patterns (AREA)
- Emergency Alarm Devices (AREA)
Abstract
The invention discloses an anti-terrorism security inspection method for explosives, which is characterized by comprising the following steps: step 1: after passenger images are collected, comparing the passenger images with personnel image information in a database; step 2: reading passenger identity card information and verifying the identity information; and step 3: extracting molecules and/or particles on the passenger identity card, and detecting and judging whether the molecules and/or the particles contain the explosive molecules and/or the particles; and 4, step 4: and when any step judges that the person is the suspicious person, outputting judgment information that the person is the suspicious person. The invention improves the people flow rate, reduces the labor cost of security inspection and improves the working efficiency by the three-in-one case measure.
Description
Technical Field
The invention relates to the technical field of comprehensive detection, in particular to a method and a system for detecting explosives, which are mainly applied to airports and railway stations and used for delineating potential dangerous persons through face recognition, certificate recognition and explosive detection.
Background
With the continuous change of the public security situation of the current society and the requirement of anti-terrorism work, more and more search and explosion anti-terrorism technical means are applied. In order to ensure safety, during related security inspection, generally, means such as dog search, biometric identification, certificate identification, X-ray detection, metal detection, chemical detection and the like are used in a combined manner, so that the detection accuracy is improved. However, each additional detection means requires at least one more detection person, which increases the detection time, and causes inconvenience to facilities such as airports and stations that deal with large passenger flows.
In addition, explosives detection instruments also typically exhibit false positives and false negatives, often due to cross contamination of the instrument itself, the sampling tool, and the operator with explosives. For an explosive detector, the detection capability of the explosive detector reaches picogram level, the higher the precision and the sensitivity of the explosive detector are, the more difficult the false alarm is to control, and after the explosive detector gives an alarm, the explosive detector usually needs to be debugged and calibrated to be normal, and during the debugging waiting period, the explosive detector can influence the passage of passengers. Further, the detection result is greatly influenced by the types of explosives, requirements on operators, working duration and surrounding environment. Explosives carried by a human body are usually subjected to sealing shielding treatment by terrorists, in large public places such as stations and airports, explosive molecules volatilized into air are diluted to volume concentration ppbv or even pptv magnitude by air circulation, and the low-concentration explosive molecules are difficult to detect by directly adopting trace detection technologies (such as ion mobility spectrometry, chemiluminescence, Raman spectrometry, terahertz spectrometry, mass spectrometry and the like).
Therefore, a detection mode with high detection speed and high precision is needed, and the security inspection strength is effectively improved.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides an anti-terrorism security inspection method and system for explosives, which are used for solving the problem that the security inspection complexity is improved while the security inspection strength is improved in the related technology.
In an exemplary embodiment of the present invention, the anti-terrorism security inspection method for explosives includes:
step 1: after passenger images are collected, the passenger images are compared with personnel image information in a database;
step 2: reading passenger identity card information and verifying the identity information;
and step 3: extracting molecules and/or particles on the passenger identity card, and detecting and judging whether the molecules and/or particles contain explosive molecules and/or particles;
the method comprises the steps of acquiring images in real time; detecting a human face in a designated area of the image; displaying the identity card to be placed in the identity card verification area, and prompting a user to place the identity card in the specified identity card verification area; the method comprises the steps of simultaneously carrying out face comparison on a specific crowd in a face image database collected in real time and an identity card image in an identity card verification area, then obtaining particle molecules on the identity card, and determining whether the personnel carry explosives through test paper and ion mobility spectrometry. For suspicious molecules, the focus is examined.
According to a second aspect of the present invention, there is provided an anti-terrorist security system for explosives. The system comprises an identity card rapid detection device 100, a computer device 200 and a camera device 201. The device 100 for rapidly detecting an identity card comprises: the device comprises a scanning device 101, a wiping sampling device 102, a detection device 103, an identity card 104, a spring device 105, a semiconductor heating sheet 106, a conveying device 107, an air direction guiding device 108, an ion mobility spectrometry device 109 and a laser device 110.
The quick identity card detection device 100 comprises a fixing device 401 and a protrusion 402.
The fixing device 401 is fixed, in order to increase the contact area and avoid the damage of the test paper, the protrusion 402 forms the contact area of the test paper 403 into a semicircular arc shape, when the identity card 104 contacts the spring device 105, the fixing device 401 releases the test paper 403, attaches the test paper to the identity card, and then exits the accommodating device 301 through the spring device 105.
The invention has the advantages that through three-in-one case measures, the people flow rate is improved, the labor cost of security inspection is reduced, and the working efficiency is improved.
Drawings
Fig. 1 is a diagram of an anti-terrorism security inspection method for explosives.
Fig. 2 is an architecture diagram of an anti-terrorist security inspection system for explosives.
FIG. 3 is a schematic view of the connection point of the test strip.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth.
Further, the drawings are merely schematic illustrations of the present invention, in which the same reference numerals denote the same or similar parts, and thus, a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
Fig. 1 is a block diagram of a flow chart of an anti-terrorism security check method for explosives in an exemplary embodiment of the invention. Referring to fig. 1, the security check process may include:
step 1: after passenger images are collected, comparing the passenger images with personnel image information in a database;
step 2: reading passenger identity card information and verifying the identity information;
and 3, step 3: extracting molecules and/or particles on the passenger identity card, and detecting and judging whether the molecules and/or particles contain explosive molecules and/or particles;
wherein the step 1 comprises: the first face image is acquired by a camera device that may use a depth camera of a KINECT.
101, acquiring surrounding environment parameters;
102, determining target shooting parameters corresponding to the surrounding environment parameters;
and 103, photographing according to the target photographing parameters to obtain the first face three-dimensional image.
Wherein the environmental parameter may be at least one of: brightness, ambient color temperature, ambient humidity, ambient electromagnetic interference intensity, and the like, without limitation. The photographing parameters may include at least one of: focal length, exposure duration, sensitivity, aperture size, and the like. The camera device may acquire the target environmental parameter through a sensor, and the sensor may be at least one of the following: ambient light sensors, temperature sensors, humidity sensors, electromagnetic interference detection sensors, and the like. The camera device can pre-store the mapping relation between the environmental parameters and the shooting parameters, further determine the target shooting parameters corresponding to the target environmental parameters according to the mapping relation, and shoot according to the target shooting parameters to obtain the first face stereo image.
And 104, preprocessing the point cloud data of the first face stereo image to obtain a corresponding stereo face depth image.
The pretreatment comprises the following steps: and determining the characteristic points of the nose tip.
The method comprises the following steps of;
first, the quantization angle: firstly [ -90 DEG, 90 DEG ]]This angular interval was equally divided by 90 equal portions. Secondly, nose tip point determination was performed: for each angle theta i By the following transformation:
maximum Z coordinate Z i Is taken as a candidate point of the nose tip point, and the coordinate of the candidate point is recorded as P i (X i ，Y i ，Z i ). Let Z be max { Z ═ Z i The vertex corresponding to Z is the nose tip point. Wherein x, y and z are three-dimensional coordinates of the stereo face, and x ', y ' and z ' are three-dimensional coordinates taking the nose tip point as the center.
The point cloud of the tip of the nose is converted to the origin and then those points more than 8 cm away from the origin are removed, so that a point cloud of only the curved surface area of the face can be obtained. And then, performing principal component analysis on the three-dimensional face data, taking the minimum eigenvalue vector as a Z axis and the maximum eigenvalue vector as a Y axis of a new coordinate system, and establishing a right-hand coordinate system. And taking the nose tip point as the origin of the whole right-hand coordinate system, converting the three-dimensional face point cloud data into the right-hand coordinate system, finishing the correction of the face posture and realizing the normalization processing of the face data.
After pose correction, a mirror point cloud is created by replacing the X value of the original point cloud with the inverse (-X). Calculating the Euclidean distance from the original point cloud (only XY value) to the nearest point, if the distance is less than a threshold value delta, deleting the mirror image point, and if and only if no neighborhood point exists at a certain position, adding the mirror image point. The remaining mirror points are then merged with the original point cloud. And performing meshing operation on the extracted three-dimensional face point cloud, performing smooth resampling on the three-dimensional face model by adopting a mesh-based smoothing algorithm, and recovering the smooth three-dimensional face mesh obtained by iterative processing into a three-dimensional face point cloud.
After the normalization processing of the face data, two nasal wing points are searched in a rectangle of 42mm multiplied by 50mm taking the tip of the nose as the center. 12 feature points including a nasal tip point, 2 nasal alar points, a left inner eye angular point, a right outer eye angular point, a middle point of the edges of an upper lip and a lower lip, a chin point and a left mouth angular point and a right mouth angular point are extracted.
And (3) selecting 8 feature points for automatically detecting the central points of the upper and lower eyelids of the left and right eyes and the inner and outer boundary points of the left and right eyebrows on a two-dimensional feature image (2D feature image) corresponding to the point cloud of the human face.
The detection of the central points of the upper eyelid and the lower eyelid of the left eye and the right eye comprises the following steps: then, the gray gradient of the eye region is calculated by using a secondary differential operator, and the process comprises smoothing denoising, gradient calculation and binarization denoising again, so that a binary image of the eye region is obtained. Then searching out an upper eyelid curve from the image after the second differentiation. Determining a starting point and an end point, taking a right eye as an example, selecting a right eye outer eye angular point mapped by the three-dimensional point cloud as the starting point, taking a right eye inner eye angular point as the end point, searching the edge of the upper eyelid from the starting point and terminating the edge with the right eye outer eye angular point, and selecting a point on a curve with an upper horizontal coordinate as the middle point of the starting point and the end point horizontal coordinate as the upper eyelid point.
And selecting three-dimensional Euclidean distances between 20 points from the obtained 20 characteristic points to form 20-dimensional vectors as characteristics.
The distance characteristics between the 20 points and the points are selected as follows
| Feature(s) | Distance between two points |
| 1 | Nose tip point-right intraocular canthus point |
| 2 | Nose tip point-angular point of inner eye of left eye |
| 3 | Left mouth corner point-chin point |
| 4 | Right mouth corner point-chin point |
| 5 | Lower lip midpoint-chin point |
| 6 | Middle of upper lip-middle of lower lip |
| 7 | Nasal tip-midpoint of upper lip |
| 8 | Left nasal wing point-left mouth corner point |
| 9 | Right nasal wing point-right mouth angle point |
| 10 | Right eyebrow outer boundary point-right eyebrow inner boundary point |
| 11 | Outer boundary point of left eye eyebrow-inner boundary point of left eye eyebrow |
| 12 | Inner boundary point of right eyebrow-inner boundary point of left eyebrow |
| 13 | Right eye inner eye angular point-left eye inner eye angular point |
| 14 | Inner boundary point of right eyebrow hair-inner canthus point of right eye |
| 15 | Inner boundary point of left eye eyebrow-inner eye corner point of left eye |
| 16 | Right eyebrow outer boundary point-right eye outer corner point |
| 17 | Midpoint of right superior eyelid-midpoint of right inferior eyelid |
| 18 | Midpoint of upper left eyelid-midpoint of lower left eyelid |
| 19 | Right eye corner point-right nose wing point |
| 20 | Right eye corner point-right nose wing point |
And comparing the similarity of the acquired 20-dimensional feature vector with the face feature vector in the database, and further judging whether the face features are similar. The acquired stereo image is preprocessed through the algorithm in the invention, and then the characteristic points are acquired for comparison, and the characteristic comparison can be rapidly carried out, thereby avoiding that the performance of two-dimensional face recognition is reduced due to factors such as illumination, posture, makeup, expression, age change and the like, and also avoiding that a large amount of three-dimensional face data is calculated, thereby saving time.
The step 2 comprises the following steps: when the information of the second generation identity card is read, the face photo on the identity card is obtained at the same time, the face photo is processed by adopting a face recognition algorithm based on deep learning, and first face characteristic data is extracted.
And identifying the identity card number information on the identity card, acquiring a certificate photo corresponding to the identity card number in a public security system database according to the identity card number after identifying the identity card number in the identity card image, and extracting the certificate photo by adopting a face recognition algorithm based on deep learning to acquire second face characteristic data.
And judging the similarity value of the first face feature data and the second face feature data. And carrying out consistency check on the identity card carrier and the identity card carried by the identity card carrier according to the similarity value comparison result.
The step 3: and extracting molecules and/or particles on the passenger identity card, and detecting and judging whether the molecules and/or particles contain explosive molecules and/or particles.
And the step 3 comprises detecting the particles on the identity card through test paper and ion mobility spectrometry so as to judge whether the person is a suspicious molecule.
Fig. 2 is an identity card rapid detection device. As shown in fig. 2, the apparatus 100 for rapidly detecting an identification card includes: a scanning device 101, a wiping sampling device 102, a detection device 103, an identity card 104, a spring device 105, a semiconductor heating sheet 106, a conveying device 107, an air direction guiding device 108, an ion mobility spectrometry device 109, a laser device 110, and a computer 200
The accommodating device 301 is used for accommodating the inserted id card to be tested, and the scanning device 101 is used for acquiring and verifying the information of the id card to be tested placed in the accommodating device.
Wiping sampling device 102 includes: for replacing the sampling test paper.
The detection device 103 includes: and the test paper is used for wiping and sampling the identity card to be detected. The test paper comprises a basal layer and hydrogel arranged in a detection reagent, and the color change of the test paper is observed, so that whether the identity card contains explosive or not is judged.
Further, when the identification card 104 is conveyed to the accommodating device 301, the detecting device 103 starts to rub and collect the particulate matter on the identification card, and after the spring device 105 ejects the identification card, the test paper is attached to the identification card and then exits the accommodating device 301. The tester can compare the color change of the test paper with a standard color card, so as to judge whether the identity card contains explosive particles.
Further, in order to prevent the test paper from being damaged during the rubbing process, the test paper fixing mode is as shown in fig. 3. One end of the test paper is fixed by a controllable fixing device 401, in order to increase the contact area and avoid the damage of the test paper, the protrusion 402 forms the contact area of the test paper 403 into a semicircular arc shape, when the identity card 104 contacts the spring device 105, the fixing device 401 releases the test paper 403 and attaches to the identity card, and then the identity card exits from the accommodating device 301 through the spring device 105.
The conveying device 107 conveys the identity card 104 by rotating the sampling belt and obtains attached particles below the identity card 104, and the semiconductor heating sheet 106 is used for heating the sampling belt and enhancing the motion activity of the attached particles on the sampling belt.
The air blowing device blows off the particle molecules on the conveying device 107 and conveys the particles carrying the particle molecules to the ion mobility spectrometry device 109. The ion mobility spectrometry device 109 comprises an ion source, an ion reaction chamber, an ion gate and an ion mobility chamber, wherein particles blown from a wind direction enter the ionization chamber, are ionized by laser emitted by a laser device 110 and then are firstly gathered in front of the ion gate, when the ion gate is opened, ions synchronously enter a mobility area, and different ions have different mobility rates, so that an ion mobility spectrogram changing along with time is obtained, an oscilloscope can be used for collecting the mobility spectrometry, the mobility spectrometry is controlled and displayed by a computer, and a detection result is output.
The particles include at least one of: gasoline molecules, kerosene molecules, alcohol gasoline mixtures, ammonium nitrate molecules, trinitrotoluene or C4 plastic explosive particles.
In other words, when the identification card is stained with the explosive composition, some fine particles or gas molecules are scattered in the explosive composition. These discrete explosive molecules and/or particles are blown away by creating an air flow in the device for rapid identification card detection. By collecting and detecting the blown-off molecules and/or particles, if they contain explosive molecules and/or particles, the person can be identified as possibly harboring explosives or being judged as a suspect.
And when any step judges that the person is the suspicious person, outputting judgment information that the person is the suspicious person.
Illustratively, the determination of the suspicious person includes sending an alarm signal, and either an audible and visual alarm device may send an audible and visual alarm in response to the alarm signal, or a security check person may view the security check signal to implement manual interception, etc. The type and mode of the alarm can be determined by the whole security check system, and a person skilled in the art can set a response mechanism to the alarm signal according to the actual situation.
According to the anti-terrorism security inspection method and the security inspection equipment suitable for the open area, molecules and/or particles blown away from a human body are collected and detected by utilizing the characteristic that the explosive components of explosives are scattered out of fine particles or molecules, and if the molecules and/or particles contain the explosive components, suspicious persons possibly hiding the explosives can be identified. The security inspection equipment is simple, and the security inspection result is reliable.
In the description of the present invention, it is to be understood that the terms "first", "second" and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (7)
1. An anti-terrorist security inspection method for explosives, comprising:
step 1: after passenger images are collected, the passenger images are compared with personnel image information in a database;
step 2: reading passenger identity card information and verifying the identity information;
and step 3: extracting molecules and/or particles on the passenger identity card, and detecting and judging whether the molecules and/or the particles contain the explosive molecules and/or the particles;
and 4, step 4: when the person is judged to be a suspicious person by one or a combination of the step 1, the step 2 and the step 3, outputting judgment information that the person is the suspicious person;
the step 1 comprises the following steps: acquiring a first face image through a camera device, wherein the camera device can use a depth camera of KINECT,
101, acquiring surrounding environment parameters;
step 102, determining target shooting parameters corresponding to the surrounding environment parameters;
103, photographing according to the target photographing parameters to obtain a first face three-dimensional image;
104, preprocessing the point cloud data of the obtained first face stereo image to obtain a corresponding stereo face depth image;
the preprocessing of the point cloud data of the obtained first face stereo image comprises the following steps:
first, the quantization angle: firstly, dividing an angle interval of [ -90 degrees and 90 degrees ] by 90 equal parts and equal amounts;
secondly, nose tip point determination was performed: for each angle
By transforming as follows:
maximum Z coordinate
The point of (a) is taken as a candidate point of the nose tip point, and the coordinate of the point is recorded as
(ii) a Order to
The vertex corresponding to Z is the nose tip point; wherein x, y and z are three-dimensional coordinates of the three-dimensional face, and x ', y ' and z ' are three-dimensional coordinates taking the nose tip point as the center;
converting the point cloud of the nose tip to an origin, and then removing points which are more than 8 centimeters away from the origin, so that the point cloud of only the curved surface area of the face can be obtained; then, performing principal component analysis on the three-dimensional face data, taking the minimum eigenvalue vector as a Z axis and the maximum eigenvalue vector as a Y axis of a new coordinate system, and establishing a right-hand coordinate system; the nose tip point is used as the origin of the whole right-hand coordinate system, the three-dimensional face point cloud data is converted into the right-hand coordinate system, the face posture correction is completed, and the face data normalization processing is realized;
after the attitude correction, creating a mirror image point cloud by replacing the X value of the original point cloud with the inverse number (-X); calculating the Euclidean distance from the original point cloud (only XY value) to the nearest point, if the Euclidean distance is smaller than a threshold value delta, deleting the mirror image point, and if and only if no adjacent point exists in a certain position, adding the mirror image point; then merging the remaining mirror image points with the original point cloud; performing meshing operation on the extracted three-dimensional face point cloud, performing smooth resampling on a three-dimensional face model by adopting a mesh-based smoothing algorithm, and then recovering a smooth three-dimensional face mesh obtained through iterative processing into a three-dimensional face point cloud;
after the human face data are normalized, searching in a rectangle of 42mm multiplied by 50mm with the tip of the nose as the center to obtain two nasal wing points; extracting 12 feature points including a nose cusp point, 2 nasal wing points, a left inner eye angular point, a right outer eye angular point, a middle point of edges of upper and lower lips, a chin point and a left mouth angular point and a right mouth angular point;
selecting 8 feature points for automatically detecting the center points of upper and lower eyelids of left and right eyes and the inner and outer boundary points of left and right eyebrows on a two-dimensional feature image corresponding to the point cloud of the human face;
the detection of the central points of the upper eyelid and the lower eyelid of the left eye and the right eye comprises the following steps: then, calculating the gray gradient of the eye region by using a secondary differential operator, wherein the process comprises smoothing denoising, gradient calculation and binarization denoising again, so as to obtain a binary image of the eye region; then searching an upper eyelid curve from the image after the second differentiation; determining a starting point and an end point, taking a right eye as an example, selecting a right eye outer eye angular point mapped by three-dimensional point cloud as the starting point, taking a right eye inner eye angular point as the end point, searching the edge of the upper eyelid from the starting point and terminating the edge with the right eye outer eye angular point, and selecting a point on a curve with an upper horizontal coordinate as the middle point of the starting point and the end point horizontal coordinate as the upper eyelid point;
selecting 20 three-dimensional Euclidean distances between 20 points from the obtained 20 characteristic points to form 20-dimensional vectors as characteristics;
acquiring three-dimensional Euclidean distances among 20 feature points in the face point cloud to form 20-dimensional vectors, and comparing the similarity of the acquired 20-dimensional feature vectors with the face feature vectors in the database to further judge whether the face features are similar;
step 3, detecting particles on the passenger identity card through test paper and ion mobility spectrometry so as to judge whether the person is a suspicious molecule;
and wiping the passenger identity card by using a test paper, wherein the test paper comprises a substrate layer and hydrogel loaded with a detection reagent, and the detection reagent is discolored after being connected with explosive molecules and/or particles.
2. The method of claim 1, wherein reading identity information comprises obtaining a passenger identification number, a passenger identification card face image.
3. The method of claim 1, wherein the extracting molecules and/or particles on the passenger identification card, and detecting and determining whether the molecules and/or particles contain explosives comprises collecting molecules and/or particles within a predetermined range on the surface of the passenger identification card; and analyzing the molecules and/or particles and outputting test data.
4. The method of claim 1, wherein the identity information is transmitted to a verification mechanism and an authentication signal is transmitted in response to a verification result of the verification mechanism.
5. The method of any one of claims 1 to 4, wherein an alarm signal is sent when any one of face recognition, the identity information, test data does not meet a preset condition.
6. A device for rapidly detecting an identification card to perform the method of claim 1, comprising: the device comprises a scanning device (101), a wiping sampling device (102), a detection device (103), an identity card (104), a spring device (105), a semiconductor heating sheet (106), a conveying device (107), a wind direction guiding device (108), an ion mobility spectrometry device (109), a laser device (110) and a computer (200).
7. The device according to claim 6, wherein one end of the strip in the detecting device (103) is fixed by a controllable fixing device (401), and in order to increase the contact area and avoid the strip from being damaged, the protrusion (402) forms the contact area of the strip (403) into a semi-circular arc shape, and when the ID card (104) contacts the spring device (105), the fixing device (401) releases the strip (403), and attaches to the ID card, and then exits the containing device (301) through the spring device (105).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911150122.8A CN111122687B (en) | 2019-11-21 | 2019-11-21 | Anti-terrorist security inspection method for explosives |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911150122.8A CN111122687B (en) | 2019-11-21 | 2019-11-21 | Anti-terrorist security inspection method for explosives |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111122687A CN111122687A (en) | 2020-05-08 |
| CN111122687B true CN111122687B (en) | 2022-09-20 |
Family
ID=70496200
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911150122.8A Active CN111122687B (en) | 2019-11-21 | 2019-11-21 | Anti-terrorist security inspection method for explosives |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111122687B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111882729A (en) * | 2020-06-30 | 2020-11-03 | 同方威视技术股份有限公司 | Gate system and control method of gate system |
| CN112634534A (en) * | 2020-12-18 | 2021-04-09 | 深圳市沐腾科技有限公司 | Storage cabinet control method and device, control equipment, storage medium and storage cabinet |
| CN113340861A (en) * | 2021-05-21 | 2021-09-03 | 上海工物高技术产业发展有限公司 | Pedestrian passageway gate system and explosive detection method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008056777A1 (en) * | 2006-11-10 | 2008-05-15 | Konica Minolta Holdings, Inc. | Authentication system and authentication method |
| CN104965235A (en) * | 2015-06-12 | 2015-10-07 | 同方威视技术股份有限公司 | Security check system and method |
| CN106570460A (en) * | 2016-10-20 | 2017-04-19 | 三明学院 | Single-image human face posture estimation method based on depth value |
| CN107247916A (en) * | 2017-04-19 | 2017-10-13 | 广东工业大学 | A kind of three-dimensional face identification method based on Kinect |
| CN208109765U (en) * | 2018-05-09 | 2018-11-16 | 清华大学 | Gate system for sample detection |
| CN108846306A (en) * | 2018-03-28 | 2018-11-20 | 中科博宏（北京）科技有限公司 | A kind of identity card Compare System and method based on deep learning recognition of face |
| CN109726617A (en) * | 2017-10-30 | 2019-05-07 | 同方威视科技江苏有限公司 | Safe examination system and safety inspection method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9959455B2 (en) * | 2016-06-30 | 2018-05-01 | The United States Of America As Represented By The Secretary Of The Army | System and method for face recognition using three dimensions |
-
2019
- 2019-11-21 CN CN201911150122.8A patent/CN111122687B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008056777A1 (en) * | 2006-11-10 | 2008-05-15 | Konica Minolta Holdings, Inc. | Authentication system and authentication method |
| CN104965235A (en) * | 2015-06-12 | 2015-10-07 | 同方威视技术股份有限公司 | Security check system and method |
| CN106570460A (en) * | 2016-10-20 | 2017-04-19 | 三明学院 | Single-image human face posture estimation method based on depth value |
| CN107247916A (en) * | 2017-04-19 | 2017-10-13 | 广东工业大学 | A kind of three-dimensional face identification method based on Kinect |
| CN109726617A (en) * | 2017-10-30 | 2019-05-07 | 同方威视科技江苏有限公司 | Safe examination system and safety inspection method |
| CN108846306A (en) * | 2018-03-28 | 2018-11-20 | 中科博宏（北京）科技有限公司 | A kind of identity card Compare System and method based on deep learning recognition of face |
| CN208109765U (en) * | 2018-05-09 | 2018-11-16 | 清华大学 | Gate system for sample detection |
Non-Patent Citations (1)
| Title |
|---|
| 基于Kinect深度图像的三维人脸识别技术研究;魏尚;《中国优秀硕士学位论文全文数据库 信息科技辑》;20130215 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111122687A (en) | 2020-05-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111122687B (en) | Anti-terrorist security inspection method for explosives | |
| US9767371B2 (en) | Systems and methods for identifying traffic control devices and testing the retroreflectivity of the same | |
| US9940515B2 (en) | Image analysis for authenticating a product | |
| CN103827916B (en) | Wrinkle detects the methods, devices and systems assessed with wrinkle | |
| US20160253807A1 (en) | Method and System for Determining 3D Object Poses and Landmark Points using Surface Patches | |
| EP3067823A1 (en) | Detection method and device | |
| US20180034852A1 (en) | Anti-spoofing system and methods useful in conjunction therewith | |
| CN111027481B (en) | Behavior analysis method and device based on human body key point detection | |
| US20080084553A1 (en) | Method And Apparatus For Multimodal Detection | |
| US20200333504A1 (en) | Millimeter Wave Image Based Human Body Foreign Object Detection Method and System | |
| US11733151B2 (en) | Assay detection, accuracy and reliability improvement | |
| US11959848B2 (en) | Method of storing and retrieving digital pathology analysis results | |
| RU2716465C1 (en) | Optical inspection method and device using it | |
| JP2022501713A (en) | Motion recognition methods and devices, electronic devices, and storage media | |
| Dumaliang et al. | Coin identification and conversion system using image processing | |
| KR20190114241A (en) | Apparatus for algae classification and cell countion based on deep learning and method for thereof | |
| CN109993107B (en) | Mobile robot obstacle visual detection method based on non-iterative K-means algorithm | |
| CN105138989B (en) | A kind of vehicle sun visor condition detection method and device | |
| JP2006293720A (en) | Face detection apparatus, face detection method, and face detection program | |
| WO2006019991A2 (en) | Method and apparatus for multimodal detection | |
| CN110766848A (en) | Analysis method based on biological characteristics | |
| Chou et al. | Toward face detection, pose estimation and human recognition from hyperspectral imagery | |
| KR101087250B1 (en) | Apparatus for detecting face using skin region detection | |
| CN111862091A (en) | Early syndrome discovery system based on phenotype measurement | |
| CN110378294A (en) | A kind of EO-1 hyperion object detection method and system based on local energy constraint and Fusion Features |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |