KR20210136771A - UBT system using face contour recognition AI and method thereof - Google Patents

Abstract

Disclosed are a ubiquitous-based test (UBT) system using face contour recognition artificial intelligence (AI) and a method thereof. According to the present invention, the UBT system comprises: an online test or UBT server registering test applicant information and photos to issue a QR code and providing a test program for performing an online test or UBT; an applicant terminal connected to the online test or UBT server through a wired or wireless communication network and including an AI-based facial recognition module and a 5-point scale cheating prevention module; and a supervisor terminal. In an online test or UBT test based on a tablet PC, smartphone, and PC including the AI-based facial recognition module and the 5-point scale cheating prevention module, an AI-based facial contour recognition technology (posenet algorithm) is used to prevent proxy test or cheating, so that the applicant terminal recognizes the contour of a test applicant face and feature points of the two eyes, the nose, and the two ears, movement of the head to a right/left side is detected and tracked according to whether three points of the eyes and nose get closer to two end points on both the ears to detect a behavioral pattern of the face. When the facial contour is not recognized during face recognition, a warning message or alarm is output or is stored in the applicant terminal if the tablet PC's camera image deviates from a test screen (when a corresponding direction exceeds a predetermined value depending on whether the three points of the eyes and nose get closer to the two end points on both the ears), so that the stored alarm or message is transmitted to the test server and scoring results are provided to the applicants.

Description

UBT system using face contour recognition AI and method thereof

The present invention relates to a UBT system using artificial intelligence and a method therefor, and more particularly, a tablet PC, smartphone, and PC-based online test and UBT test equipped with an artificial intelligence facial recognition module and a 5-point scale anti-fraud module. In this study, using AI-based facial contour recognition technology (posenet algorithm) to prevent surrogate testing or cheating, the test taker's terminal recognizes the contour of the test taker's face and the eye2/nose/ear2 feature points, and the 3 eye/nose points are positive. It is equipped with an artificial intelligence facial recognition module and a 5-point scale anti-fraud module that detects facial behavior patterns by detecting and tracking head movement to the right/left depending on whether the tip of the ear approaches 2 points. If it is not recognized, when the tablet PC's camera image deviates from the test screen (when the release direction exceeds a certain value depending on whether the 3 eyes/nose points get closer to the 2 points on both ends), a warning message or alarm is output or Or it relates to a tablet PC, smartphone, PC-based UBT system using facial contour recognition artificial intelligence, which stores the information in the information device of the test taker terminal, transmits it to the test server at the end of the test, and provides scoring results to test takers, and a method therefor.

Face recognition technology is mainly used for face recognition based on an appearance based matching method and a feature. Face recognition is recognized differently depending on the camera's shooting angle, lighting direction, posture, facial expression changes, and face changes over time.

In the feature-based face recognition, a detection method using haar-like features for image data captured by a digital camera, a camera of an IoT device, or a camera of a smartphone, and a detection method using a Modified Census Transform (MCT) image are used. . Using the face and eye detector learned as Haar-like features from the input image of the smartphone camera, the contour of the face and the forehead/eyes/nose/mouth are detected, and the region of interest (ROI) is used to detect the circular pupil. The eye area set as Interest) is converted to grayscale, and the histogram [x-axis pixel value of each pixel, The number of corresponding pixel values on the y-axis] is calculated and the image of the eye is binarized, and then the photo of the eye area is preprocessed through histogram equalization, and the eyebrows, eyes, nose, mouth, and A face is recognized by detecting the feature data of the chin, extracting texture features and shape features, and comparing the similarity with feature points of the face photos stored in the face recognition DB.

The eyebrow, eye, nose, mouth, and chin feature values of the face region are expressed as the difference between the sum of pixels included in the white region of the Haar-like feature and the sum of pixels included in the black region.

For example, the distance from the eye region detected in the face region photo of standard size to both endpoints of the right and left eyes, and the size value of the iris extracted using the hough circle transform algorithm are characterized. used as a value.

As a related prior art 1, Patent Publication No. 10-2017-0050465 discloses "a face recognition apparatus and method".

According to this embodiment, in recognizing a face from an input image using machine learning, the face recognition rate is improved by normalizing the face pose and perspective, and the face learning data is obtained by automatically generating a virtual face image as the face learning data. A face recognition device and method are provided for saving time and cost.

1 is a block diagram of an existing face recognition device.

The face recognition device 100 includes an image display device, an image photographing device, a face recognition server, a tablet PC, a laptop, a personal PC, a smart phone, a personal digital assistant (PDA), a mobile communication terminal, and It may be any one of intelligent robots and the like.

The face recognition apparatus 100 includes an input image acquisition unit 112 for acquiring an input image input from a camera; A front pose image is generated by detecting a face region in the input image and normalizing a face pose, and the perspective of the front pose image is removed to remove perspective distortion according to the distance between the camera and the subject. ) by normalizing the normalization unit 114 to generate a normalized image; a feature vector extracting unit 116 for extracting a feature vector representing the face of the subject from the normalized image; and a face recognition unit 118 for recognizing the face of the subject included in the input image by applying the feature vector to the previously learned classification model.

The input image acquisition unit 112 acquires an input image input from the camera. The camera may be a depth recognition camera, a stereo camera, and a color camera (eg, a Kinect camera, etc.). In addition, the input image is an image including the face of a subject to be recognized, a two-dimensional still image and Includes video. The input image may include a color image, a depth image, and a color-depth (RGB-D) image.

The normalizer 114 generates a normalized image by detecting a face region from the input image and normalizing a face pose and perspective. When there is a change in the face pose, the face recognition rate is lowered because the gray scale, shape, position of feature points, etc. change. Also, if the distance between the camera and the subject is different, even if the same subject is photographed, perspective distortion (distortion, for example) occurs differently for each photographed location, so it may appear as if a different subject was photographed. Therefore, in order to improve the face recognition rate, it is necessary to normalize the face pose and perspective of the input image.

The normalization unit 114 inputs the face images for learning of various poses to the input layer of the first artificial neural network, and trains the first artificial neural network so that the face images for learning of the front pose are output from the output layer of the first artificial neural network. face pose normalization learning unit; and inputting the data output from the output layer of the first artificial neural network to the input layer of the second artificial neural network, and learning the second artificial neural network so that a face image for learning without perspective distortion is output from the output layer of the second artificial neural network. Includes a perspective normalization learning unit.

The normalization unit inputs face images for learning of various poses with various perspective distortions to the input layer of an integrated artificial neural network that integrates the first artificial neural network and the second artificial neural network in which learning is completed, and there is no perspective distortion of the front pose. The integrated artificial neural network is trained so that the face image for learning is output from the output layer of the integrated artificial neural network.

The feature vector extractor 116 is determined through machine learning and extracts a feature vector representing the face of the subject from the normalized image.

The feature vector is a vector having feature values used for face recognition as elements. As a filter used to extract the feature vector, there are Gabor filter, Haar filter, LBP (Local Binary Pattern) - including DLBP (Discriminative LBP), ULBP (Uniform LBP), NLBP (Number LBP) - etc., but must be limited to this and other filters may be used.

The face recognition unit 118 recognizes the face of the subject included in the input image by applying the feature vector extracted by the feature vector extraction unit 116 to the previously learned classification model. The pre-trained classification model may include a support vector machine (SVM), a linear discriminant analysis (LDA), and a softmax, but is not necessarily limited thereto.

The virtual face image generator 124 may generate a plurality of virtual face images used for learning by the normalizer 114 , the feature vector extractor 116 , and the face recognizer 118 .

The plurality of virtual face images means a face image generated by transforming a three-dimensional face model synthesized by the virtual face image generator 124 using one or more two-dimensional reference images obtained from a camera.

* Features of face recognition system

The recognition rate changes depending on the lighting or distance from the camera.

The shape of the face continues to change according to the angle, expression, and age of the face, and may be recognized differently according to changes in the appearance such as eyebrows, glasses, and makeup.

* Configuration steps of face recognition system

- Image acquisition: Image acquisition from CCD or CMOS Image Sensor camera

- Pre-processing: noise-removing image processing and segmentation

- Face detection: Detects the face area of the ROI area from the input image

- Face normalization: standard size, feature extraction, brightness and geometric normalization

- Face recognition: comparison and recognition of detected images and database images

* Geometric method

- Features: Recognize by extracting geometric feature points of the face and determining whether they match

- Performance: Because the face is three-dimensional and can be rotated, there is a limit to its application.

* Eigenfaces: Developed by Pentland in 1991.

- Features: PCA (Principal Component Analysis) is applied by extracting key points, and Euclidean distance is applied by measuring similarity.

- Performance: A representative face recognition method that responds sensitively to lighting or environmental changes, but has been widely used and has proven its performance.

* Fisherfaces

- Features: Face recognition method using FLD (Fisher Linear Discriminant) as a classification algorithm.

- Performance: An algorithm that is more accurate and insensitive to environmental changes by learning the characteristics of each person, and can be applied in real time in an on-line state.

* Method based on SVM (Support Vector Machine)

- Features: Algorithm using PCA and SVM (Support Vector Machine) algorithm.

- Performance: Algorithm that has a relatively high recognition rate, but consumes a lot of time and memory to be applied to multi-class applications such as face recognition. A representative algorithm of the face recognition algorithm currently being studied.

* Neural network

- Features: Search for learned face shapes using sliding windows in black and white still images

- Performance: It is possible to extract two or more faces, but the speed is slow and learning is difficult.

* Fuzzy + Neural Network

- Features: As the input of the neural network circuit, the fuzzy membership function is used instead of the pixel brightness value.

- Performance: Performance is improved compared to the method using only neural networks, but processing speed is lower.

* Wavelet + Elastic Matching

- Features: It uses frequency conversion and is effective in handling changes in posture and expression.

- Performance: The amount of computation is high compared to the recognition rate.

As a related prior art, in Patent Registration No. 10-1765770, a system and method for detecting cheating of an online test using image processing and a recording medium storing a program for implementing the same are registered.

The online test cheating detection system and method can supervise online exam cheating and secure the reliability and authenticity of the online exam by using image processing to detect suspected cheating of the examinee during the online exam. Provided are a system and method for detecting cheating of an online test using image processing that can be performed, and a recording medium storing a program for implementing the same.

1 is a block diagram of an online evaluation system including a conventional online test cheating detection system.

The online evaluation system includes an examinee terminal 110 for an examinee to take an online exam; a camera 120 attached to the examinee's terminal or provided in a smart device to photograph the front of the examinee; a cheating detection server 100 that receives image information photographed by the examinee from the camera in frame units, and detects cheating through image processing; When the cheating detection server determines that the test taker's cheating is suspected, a supervisor terminal 130 for notifying the supervisor; and an online evaluation server 140 for examining the questions of the online test taken by the examinee.

The cheating detection server 100 receives the image information of the examinee through the communication network, and when it is determined that cheating is suspected, it sends a cheating detection message to the supervisor, and sends it to the online evaluation server 140 . Transceiver 101 for notifying to stop the online test; a storage unit 102 for storing image information and coordinate information of the examinee received through the transceiver unit 101; a face recognition unit 103 for defining a rectangular area including a face area from the received image information of the examinee; a pupil coordinate obtaining unit 104 for setting pupil coordinates of both eyes by image processing the shape and contrast of both eyes in the coordinate system based on the rectangular region; An intermediate coordinate obtaining unit 105 that determines the intermediate coordinates of the pupil coordinates of both eyes, a background recognition unit 106 that sets the background for all areas except for the body and face areas of the examinee, and the face recognition unit ( 103) whether there are two or more face regions, the real-time pupil coordinates based on the reference pupil coordinates by comparing the reference pupil coordinates received from the pupil coordinate obtaining unit 104 with the average values of the real-time pupil coordinates Whether the equilibrium value is outside the preset error range, whether the movement distance of the intermediate coordinates received from the intermediate coordinate obtaining unit 105 is out of the preset error range, and the background received from the background recognition unit 106 and a misconduct determination unit 107 for determining whether a change in the area is detected.

In the example, the rectangular area including the face area was defined based on the content disclosed in the paper "Robust Real-Time Face Detection Using Face Certainty Map" (Volume 4642 of the series Lecture Notes in Computer Science pp 29-38).

The fraud detection server 100,

In the frame-by-frame image information taken by the examinee, a rectangular area including the examinee's face area is defined, and the shape and contrast of both eyes are image-processed in the coordinate system based on the rectangular area, and both end points (310) of both eyes. , 320), set the center points 350 and 360 of the black regions 330 and 340 located between them as the pupil coordinates of both eyes, and calculate the average value of the pupil coordinates of the examinee for a certain period of time before the start of the test to obtain a reference pupil coordinate value, and when the test starts, the average value of the pupil coordinates of both eyes of the examinee is calculated at a predetermined time unit to obtain the average value of the real-time pupil coordinates, and the real-time average value of the pupil coordinates is calculated based on the reference pupil coordinates. If it is out of the set error range, it informs the supervisor terminal,

The center point 370 of the pupil coordinates of both eyes is determined as the intermediate coordinate,

The movement direction and movement distance of the intermediate coordinates are tracked in units of frames, and when the movement distance of the intermediate coordinates is out of a preset error range, the examinee's head rotation direction or movement direction is notified, and the examinee's frame unit image information If there are two or more face regions obtained from

It is characterized in that the background is set for all areas except the body and face areas of the examinee, and when a change in the background is detected during the test time, it is notified to the supervisor terminal,

The fraud detection server 100,

When the examinee's image information is received through the communication network and it is determined that cheating is suspected, a fraudulent detection message is sent to the supervisor terminal 130, and the examinee's online exam questions are asked. to the online evaluation server 140 to stop the online test.

When the online evaluation server 140 receives a message from the cheating detection server 100 indicating that a suspected cheating operation has been detected while taking the question of the online test taken by the examinee, the online test of the examinee It is characterized in that after stopping the question-taking, and after receiving a message from the supervisor terminal 130 or the cheating detection server 100 that the question may be restarted, the online test question-taking is restarted.

Recently, in face recognition technology, head/eye/nose/mouth features using HOG, LBP, and Gabor feature extraction algorithms, which were the mainstream hand-crafted features, are deep learning-based features such as face detection, CNN It uses facial landmark detection and facial feature extraction and classification technology using (Convolutional Neural Network) algorithm to provide results similar to 9753% of human recognition level.

In 2014, AI facial recognition technologies such as Facebook's DeepFace were launched. In the case of DeepID1 ["Deep Learning Face Representation from Predicting 10,000 Classes," CVPR 2014 ] announced at CVPR, it uses 39x31 pixels, which is smaller than DeepFace's 152x152 pixels of input resolution.

In addition, the face detector based on AlexNet and AlexNet trained in advance with ImageNet training data are fine-tuned with face images, so the detection performance is not yet high. are doing

Patent Publication No. 10-2017-0050465 (published on May 11, 2017), "Face Recognition Device and Method", SK Telecom Co., Ltd. Patent Registration No. 10-1765770 (Registration Date August 01, 2017), "A system for detecting cheating on an online test using image processing and a method therefor and a recording medium storing a program for implementing the same", Bukyung Son

An object of the present invention to solve the above problems is to prevent proxy tests or cheating in tablet PC, smartphone, PC-based online tests and UBT tests equipped with artificial intelligence facial recognition module and 5-point scale anti-fraud module The AI-based facial contour recognition technology (posenet algorithm) is used to recognize the contour of the test taker's face and the eye2/nose/ear2 feature points in the test taker terminal, and depending on whether the 3 eye/nose points are close to the 2 ear points at both ends It is equipped with an artificial intelligence facial recognition module and a 5-point scale anti-fraud module that detects and tracks head movement to the right/left and detects facial behavior patterns. When the image is taken out of the test screen (when the release direction exceeds a certain value depending on whether the 3 eyes/nose points get closer to the 2 points on the ears), a warning message or alarm is output or stored in the information device of the candidate terminal It provides a tablet PC, smartphone, and PC-based UBT system using facial contour recognition artificial intelligence that transmits to the test server at the end of the test and provides scoring results to test takers.

Another object of the present invention is to provide a tablet PC, smartphone, PC-based UBT providing method using facial contour recognition artificial intelligence.

In order to achieve the object of the present invention, the UBT system using facial contour recognition artificial intelligence registers test taker information and photos, issues a QR code, and provides a test program for conducting an online test or UBT test. UBT test server; a test taker terminal connected to the online test or UBT test server through a wired/wireless communication network, and having an artificial intelligence facial recognition module and a 5-point scale cheating prevention module; and a supervisor terminal connected to the online test or UBT test server through a wired/wireless communication network.

In order to achieve another object of the present invention, the UBT providing method using facial contour recognition artificial intelligence includes the steps of registering member information, logging in/user authentication, registering test taker information and photos as an online test or UBT test server, and storing ; issuing, by the online test or UBT test server, a QR code corresponding to candidate information and a photo; announcing, by the online test or UBT test server, a test schedule and location according to the host test; In the test taker terminal equipped with a face recognition module using AI-based facial contour recognition technology, the face recognition result of the front photo of the camera is received from the test server, and the proctor terminal compares the test taker information with the photo to confirm whether the test taker is the test taker. and deciding whether to take the exam or not; The facial recognition module of the test taker terminal and the 5-point scale anti-fraud module recognize the outline of the test taker's face and the eye2/nose/ear2 feature points, and the right / Detects head movement to the left and detects abnormal behavior patterns on the face. receiving a warning message or alarm from the candidate terminal when a warning message or alarm is output to prevent cheating); and a test program (App) and an online test paper to the test taker terminal and the proctor terminal, and stores and manages test taker information, a photo of the test taker's on-site face, and proctor information in the test server's database, when performing an online test or UBT test After storing the answers written on the test paper in each test taker terminal for a certain test time, receiving and storing the answers from the test taker terminal to the test server at the end of the test, and providing the scoring results of the answers written on the test paper by the test takers to the test taker terminal.

The UBT system and method using the facial contour recognition artificial intelligence of the present invention is a tablet PC, smartphone, and PC-based online test and UBT test equipped with an artificial intelligence facial recognition module and a 5-point scale anti-fraud module, a proxy test or To prevent cheating, AI-based facial contour recognition technology (posenet algorithm) is used to recognize the contour of the test taker's face and eye2/nose/ear2 feature points in the test taker terminal, and 3 eyes/nose points to 2 points on both ends of the ears It is equipped with an artificial intelligence facial recognition module and a 5-point scale anti-fraud module that detects facial behavior patterns by detecting and tracking head movement to the right/left depending on how close it is. If the tablet PC's camera image deviates from the test screen (when the release direction exceeds a certain value depending on whether the 3 eye/nose points get closer to the 2 ear points on both ends), a warning message or alarm is output or the test taker's terminal It is saved in the information device and transmitted to the test server at the end of the test, provides scoring results to test takers, and has the effect of preventing proxy tests or cheating in online tests and UBT tests.

In the test taker terminal, a proxy test prevention and cheating prevention technique was implemented by combining an artificial intelligence facial recognition module and a cheating prevention module using a 5-point scale.

The AI-based facial contour recognition technology used in the face recognition module of the test taker terminal and the anti-fraud module using a 5-point scale uses the posenet algorithm to recognize the facial contour of the camera image of the tablet PC. During the online test or UBT test, AI-based facial recognition technology is used to identify the tablet PC test taker through the proctor terminal linked to the test server and prevent proxy tests. It is effective in preventing proxy testing or cheating.

1 is a block diagram of an existing face recognition device.
2 is a block diagram of an online evaluation system including a conventional online test cheating detection system.
3A is a view showing the concept of an artificial intelligence platform for recognizing facial contours having a tablet PC, a smartphone, and a PC-based test online test/UBT test server during the language, health and medical education online test/UBT test.
Figure 3b is a diagram showing the face recognition function on the ubiquitous-based learning (UBL) and ubiquitous-based testing (UBT) NSDAI platform.
3c and 3d show the goals and User Experience (1. User Registration->2. Learning-> 3. QR Codes of UBI cloud App and NS facere platform used in tablet PCs, smartphones, and PCs during the online test/UBT test) Create -> 4. UBT App/Web login (QR code/passwd, ID/passwd) -> 5. Supervisor confirmation and test-> 6.7 Recognition of facial contours of UBT App/Web camera 8. Test end), Fig. 3e is a diagram showing the UI design.
Figure 4 is a diagram showing the process of 1) candidate registration, 2) learning, 3) QR code issuance, 4) QR code and face recognition, 5) supervisor confirmation (respondent face/candidate information), 6) test taking - process.
5 is a screen of candidate registration/learning/QR code issuance during the online test/UBT test.
6 is a screen in which similarity is measured through AI-based facial contour recognition of a test taker's face.
7 is a program test screen.
8 shows test server access/login/left-QR code recognition/QR code recognition during online test or UBT test, greeting sound (TTS)/right-face recognition (smartphone/tablet PC front camera)/face in the background This is a demonstration screen including the area recognition start/recognition rate display process.
9 is a block diagram of a tablet PC, a smartphone, and a PC-based UBT system using facial contour recognition artificial intelligence according to the present invention.
10 is a flowchart illustrating a tablet PC, smartphone, and PC-based UBT providing method using facial contour recognition artificial intelligence according to the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the description of the present invention, if it is determined that a detailed description of a related known function or known configuration may unnecessarily obscure the subject matter of the present invention, the detailed description thereof will be omitted. In addition, when the accompanying drawing numbers indicate the same configuration, the same reference numbers are assigned in different drawings.

9 is a block diagram of a tablet PC, a smartphone, and a PC-based UBT system using facial contour recognition artificial intelligence according to the present invention.

The UBT system and method using the facial contour recognition artificial intelligence of the present invention is a tablet PC, smartphone, and PC-based online test and UBT test equipped with an artificial intelligence facial recognition module and a 5-point scale anti-fraud module, a proxy test or To prevent cheating, AI-based facial contour recognition technology (posenet algorithm, machine learning model which allows for real-time face pose estimation) is used to detect the contour of the test taker's face and eye2/nose/ear2 feature points on the test taker terminal. It is equipped with an artificial intelligence facial recognition module and 5-point scale anti-fraud module that detects and detects facial behavior patterns by detecting and tracking head movement to the right/left depending on whether the 3 eyes/nose points get closer to the 2 ear points at both ends. If the facial contour line cannot be recognized during face recognition through FACE RECOGNITION/FACE MOTION RECOGNITION/RESULT ANLAYSIS, or the tablet PC camera image is taken away from the test screen (3 eyes/nose points are close to 2 points on both ends of the ears) If the liberation direction exceeds a certain value depending on the outcome of the test, a warning message or alarm is output or stored in the information device of the test taker's terminal and sent to the test server at the end of the test, and scoring results are provided to test takers.

In the test taker terminal, we implemented a proxy test prevention and cheating prevention technique that combines an artificial intelligence facial recognition module and a cheating prevention module using a 5-point scale.

The AI-based facial contour recognition technology used in the face recognition module of the test taker terminal and the anti-fraud module using a 5-point scale uses the posenet algorithm to recognize the facial contour of the camera image of the tablet PC. During the online test or UBT test, AI-based facial recognition technology is used to identify the tablet PC test taker through the proctor terminal linked to the test server and prevent proxy tests. Avoid proxy testing or cheating.

The test paper questions of the test program (App) are displayed with multimedia test questions including text, images, VR/AR contents, audio and video as well as text and images for each question.

1. Artificial intelligence hub platform

NSD-AI (NSDevil's Artificial intelligence) 플랫폼 기반 인공지능 허브플랫폼

NSD-AI (NSDevil's Artificial intelligence) platform-based artificial intelligence hub platform

의학교육 부문 구문 인식을 통한 교수자 채점 가이드(인제대학교 의과대학 외 공동연구)

Instructor scoring guide through phrase recognition in medical education sector (joint research with Inje University College of Medicine and others)

사물인식을 통한 체험학습 지원시스템(펀에듀랩 및 한국민속촌 외)

Experiential learning support system through object recognition (FunEdu Lab and Korean Folk Village, etc.)

치아 이미지 분석을 통한 치과교육 부문 질병분석 기능(연세대학교 치과대학 외 3개국 기관 공동연구)

Disease analysis function in the dental education sector through tooth image analysis (joint research with Yonsei University College of Dentistry and other institutions in 3 countries)

UBT connect platform 기반

Based on UBT connect platform

태블릿 PC 기반 평가 플랫폼

Tablet PC-based evaluation platform

어학평가 부문(말하기/듣기/읽기/쓰기)

Language Assessment (Speaking/Listening/Reading/Writing)

보건의료평가 부문(듣기/읽기/쓰기)

Health care evaluation sector (listening/reading/writing)

기타 직무평가 부문(읽기/쓰기)

Other job evaluation areas (read/write)

Tablet PC, smartphone, and PC-based UBT system using artificial intelligence facial contour recognition

The online test or UBT test server 100, the proctor terminal 200, and the test taker terminals 300, 310, 311 using a PC, a smartphone, and a tablet PC, and having a face recognition module and a misconduct prevention module using a 5-point scale are included. do.

The UBT system using facial contour recognition artificial intelligence registers and stores test taker information and photos, issues a QR code, and provides a greeting through TTS conversion technology after recognizing each test taker's QR code. An online test or UBT test server 100 that provides a test program for conducting a; Candidate terminals (300, 310, 311) connected to the online test or UBT test server 100 through a wired/wireless communication network, and having an artificial intelligence facial recognition module and a 5-point scale anti-fraud module; and a supervisor terminal 200 connected to the online test or UBT test server 100 through a wired/wireless communication network.

The test taker terminals 300, 310, and 311 use any one of a tablet PC, a smartphone, and a PC on which the test program (App) is installed, and have an artificial intelligence face recognition module and a 5-point scale anti-fraud module, and the face recognition module The AI-based facial contour recognition technology used in the posenet algorithm (machine learning model which allows for real-time face pose estimation) is used.

In the embodiment, the test taker terminal used a tablet PC.

The test taker terminals 300 , 310 , and 311 use the facial contour recognition technology (posenet algorithm) for the camera image by the face recognition module and the cheating prevention module. Detects head movement and detects abnormal behavior patterns on the face, and when facial contours are not recognized during face recognition, when the test screen deviates from the test screen If the threshold is exceeded), a warning message or alarm is output to prevent cheating.

(Example)

We used TensorFlow for estimating poses of human faces in Android using PoseNet models. For reference, PoseNet is a vision model that detects the position of major body parts and estimates a human pose from an image or video. TensorFlow Lite provides an Android sample application that utilizes a candidate's device's camera to detect and display major human body parts in real time. This model can estimate the position of a person's elbow and/or knee from the image, and in the embodiment, the facial contour pose estimation model was implemented by identifying the eyes/nose/ears in real time during face recognition.

The posebet algorithm used in the artificial intelligence facial recognition module uses a 5-point scale anti-fraud module for left eye (leftEye), right eye (rightEye), nose (nose), left ear (leftEar), and right ear (rightEar). implemented,

After finding the x,y coordinates of leftEar and the x,y coordinates of rightEar, you can get the diameter of the circle based on these and make an angel ring with ellipse(). The diameter can be easily obtained with dist() in p5js without having to use the Pythagorean theorem.

<Connect ml5js and webcam>

<script src="https://unpkg.com/ml5@0.3.1/dist/ml5.min.js"></script>

let video;

let poseNet;

function setup() {

createCanvas(400, 400);

video = createCapture(VIDEO);

//Use to prevent the video from appearing twice

video.hide();

poseNet = ml5.poseNet(video,modelLoaded);

poseNet.on('pose',gotPoses);

console.log(ml5);

}

// Pose related callbacks are entered

function gotPoses(poses) {

}

// Used to check if the loading was successful

function modelLoaded() {

console.log('Model Loaded');

}

function draw() {

// Draw the webcam at position 0,0.

image(video,0,0);

}

The face recognition module of the test taker terminal recognizes a face through facial contour line/face feature 5-point recognition (Eye2/Nose/Ear2).

The face area is extracted in real time from the front camera face image of the test taker terminal, adjusted to the standard size of the face photo through size correction/rotation/angle correction, and compared with the learning data of the pre-stored standard size face photo DB of the test server - > Learning model of face photo -> Extracts and classifies the contour feature points of 5 facial features (Eye2/Nose/Ear2), and the contour of face recognition data at time t and t+1 The group clustering (center of cluster k-means algorithm) density estimation of the feature points of the contour of the ear 2 is performed, for example, the distance between the left eye and the left ear, the distance between the pupil center of the left eye and the right eye, and the distance between the right eye and the right ear. Measure the distance (Euclidean distance) between clusters, such as distance, and compare it with the standard size face photo stored in the face photo DB of the test server to measure the similarity by comparing the distance between the feature points.

For example, if the direction of the face is changed to the test screen of the test taker terminal (tablet PC) or the face is turned, the feature points of the eye/nose/mouth outline in the face photo may not be recognized, or the user's face and smartphone may not be valid. If the angle toward the test program changes within the distance (20-30cm), it is recognized as cheating.

Online test or UBT test server 100 is WWW server 101, control unit 102, member registration unit 103, user authentication unit 104, test notification unit 105, QR code management unit 106, greetings Study (TTS) 107 , supervisor confirmation unit 108 , face recognition unit 109 , cheating processing unit 110 , test management unit 111 , exam information DB 120 , test taker DB 121 , and face DB 123 is included.

The online test or UBT test server 100 is

a WWW server 101 connected to a test taker terminal and a supervisor terminal using any one of a tablet PC, a smartphone, and a PC through wired/wireless communication;

a control unit 102 for controlling to provide online or ubiquitous-based learning (UBL) and test (UBT) to the test taker terminal and the supervisor terminal of a tablet PC, a smartphone, and a PC through a wired/wireless communication network;

a member registration unit 103 connected to the control unit 102, receiving member information of test takers, storing and managing ID/Paaswd;

a user authentication unit 104 connected to the control unit 102 and authenticating a user using a QR code/Passwd or ID/Passwd;

A test notification unit connected to the control unit 102 and providing a schedule and location of a tablet PC, a smartphone, a PC-based online learning (Learning) and test or ubiquitous-based online learning (UBL) and a test (UBT) ( 105);

a QR code management unit 106 connected to the control unit 102, issuing a QR code corresponding to test taker information to test takers, and managing the QR code;

a greeting providing unit (TTS) 107 that provides a greeting through a TTS conversion technology after QR code recognition;

It is connected to the control unit 102, receives a photograph of the candidate from the camera from the candidate terminal, compares the candidate information stored in the database with the photograph of the candidate, and the supervisor confirms whether the examination is a proxy test and confirms the qualification to take the examination a supervisor confirmation unit 108;

A face recognition unit ( 109);

It is connected to the control unit 102 and uses the facial contour recognition technology (posenet algorithm) for the front camera image by the face recognition module and the cheating prevention module in the test taker terminal, so that 3 eyes/nose points are close to 2 points on both ends of the ears. Detects head movement to the right/left and detects abnormal behavior patterns on the face depending on whether or not it is lost When a warning message or an alarm is output to prevent cheating (when the liberation direction exceeds a certain reference value depending on whether or not the person loses), the cheating processing unit 110 receives a warning message or an alarm from the test taker terminal;

It is connected to the control unit 102, provides a test program (App) and test papers to the test taker terminal and the proctor terminal, manages test taker information, a photo of the test taker's on-site face, and proctor information, and performs an online test or UBT test. a test management unit 111 that stores and manages test paper written answers in each test taker's terminal within a certain test time, and is transmitted to the test server at the end of the test, and stores and manages test takers' written answers, scoring results, supervisor information and test taker status information;

Test information DB 120 for storing test takers' test papers, written answers, and scoring results; It includes a candidate DB 121 and a face DB 123 for storing candidate information and a standard-sized frontal face photograph.

Candidate terminals include smart phones and tablet PCs for online tests (Internet Based Tests) or ubiquitous-based tests (UBTs), as well as mobile communication terminals and laptop computers that can connect to the Internet.

The test taker terminal uses any one of a tablet PC, a smartphone, or a PC on which the test program (App) is installed. The test program (App) downloaded from the online test or UBT test server 100 is installed,

The AI-based facial contour recognition technology used in the facial recognition module uses the posenet algorithm.

The online test or UBT test server 100 provides medical school/dental college/pharmacy college/technological college exams, TOEIC/TOEFL exams, language exams, civil service exams, certification exams, health and medical education learning and exam papers. The bank announces the test schedule and location for various accredited certification tests or non-accredited tests, and provides online tests or UBT tests to test takers terminals through wired/wireless communication networks using the test program in the test server's database.

When the test schedule is confirmed, the controller 102 appoints a proctor, provides the proctor appointment information to the proctor, and notifies the test takers of the test schedule and location through a text message/webpage, and the proctor terminal 200 at the test site on the day of the test. ) to transmit and receive test information and test paper information to conduct and manage the test, and provides the scoring results from the results of the automatic scoring result unit 124 and the inspection management unit 125 to the test taker terminals.

The test information stored in the test information database 120 includes test title, test schedule and location, test time, location information of the test location, assigned proctor information and test takers list, number of seats per test room, proctor information, A/ Includes question information, answer information, scoring information, and supervisor information on each type of test paper.

In addition, the candidate database 121 includes candidate information including name, resident registration number, home address, mobile phone number, e-mail, etc., candidate photograph (standard-sized face front photograph), QR code/passwd, ID/passwd identification information, and test Relevant information and candidate application status are saved.

10 is a flowchart illustrating a tablet PC, smartphone, and PC-based UBT providing method using facial contour recognition artificial intelligence according to the present invention.

How to provide tablet PC, smartphone, and PC-based UBT using facial contour recognition artificial intelligence

After receiving member information registration and login/user authentication, the online test or UBT test server registers and stores test taker information and photos (S10);

issuing a QR code corresponding to the candidate information and the photo (S20);

The online test or the UBT test server notifying the test schedule and location according to the subjective test (S30);

Taking a QR code with a smartphone and placing it on the recognition screen on the camera of the test taker terminal (PC, tablet PC, etc.) is operated, and after recognizing the QR code, providing a greeting through a Text To Speech (TTS) function (S40);

In the test taker terminal equipped with a face recognition module using AI-based facial contour recognition technology (posenet algorithm), the face recognition result of the front photo of the camera is received from the test server, and the proctor terminal compares the test taker information with the photo to determine whether the test taker is the test taker. Checking to prevent a proxy test and determining whether to take the test (S50);

The facial recognition module of the test taker terminal recognizes the outline of the test taker's face and the eye2/nose/ear2 feature points, and detects head movement to the right/left according to whether the 3 eye/nose points get closer to the 2 ear points on both ends of the face. Detects abnormal behavior patterns of , when facial contours cannot be recognized during face recognition, when the test screen deviates from the test screen of the test taker terminal case) when a warning message or alarm is output to prevent cheating, receiving a warning message or alarm from the test taker terminal (S60);

The test program (App) and online test paper are provided to the test taker terminal and the proctor terminal, and the test taker information, the on-site face photo of the test taker, and the proctor information are stored and managed in the test server database. After storing the test paper answers in each test taker terminal for a period of time, when the test is finished, the test paper is transmitted from the test taker terminal to the test server and stored (S70), and the scoring results of the test takers' written answers are provided to the test taker terminal (S80). .

The candidate terminal uses the posenet algorithm to recognize the facial contours of the camera image of the tablet PC as an artificial intelligence (AI)-based facial contour recognition technology. During an online test or UBT test, AI-based facial recognition technology is used to identify the tablet PC test taker through the proctor's terminal linked to the test server, prevent proxy tests, and facial recognition of the test taker's face online or at the UBT test center It can be used as a control system and prevents proxy tests or cheating in online and UBT tests.

The test taker terminal uses any one of a tablet PC, a smartphone, and a PC in which the test program (App) is installed, and includes the artificial intelligence facial recognition module and the eye/nose/ear 5-point scale anti-fraud module, and the The test program is installed,

The AI-based facial contour recognition technology used in the facial recognition module uses the posenet algorithm.

The test taker terminal uses a facial contour recognition technology (posenet algorithm) for the camera image by the face recognition module and the cheating prevention module, and moves the head to the right/left according to whether the 3 eyes/nose points get closer to the 2 points on both ends of the ears Detects and detects abnormal behavior patterns of the face, and when facial contours are not recognized during face recognition, when the test screen deviated If exceeded), a warning message or alarm is output to prevent cheating.

It is operated by taking the QR code with a smartphone and placing it on the recognition screen of the candidate terminal's camera, and after recognizing the QR code, further comprising the step of providing a greeting through a Text To Speech (TTS) function.

The test paper questions of the test program can be displayed by asking not only text and images for each question, but also multimedia test questions including text, images, VR/AR contents, and audio and video.

In an embodiment, when the test taker applies for the test, the test application information is transmitted to the test server, the test application reception process for performing the test taker; a test information notification process of notifying test takers of the test schedule, time and place information including test schedule, time and location information to test takers of the test when the test schedule, time, and location are determined; After the test location and time are set, the proctor setting process is completed by setting the date and time of the set test and the proctor for each test room, providing the proctor appointment information notifying the proctor of the appointment, and receiving the appointment confirmation information from the proctor to the test server. the supervisor setting process; On the day of the test, the test site proctor waits for input of test site setting information, and when the test site setting information is input from the proctor, the test program (app, App) is provided and installed on the test takers' smartphones, and the smart phone of the test takers The test site setting process that requests test access confirmation from test takers connected through the test program installed in the phone. When the test site setting is completed, the registered and stored enforcement is performed by referring to the set question type and problem selection setting information (difficulty level). The test process of selecting any one of the list of questions to be asked, communicating with the installed test program to provide test questions, and the test taker terminal receiving the answer information from the test paper through the test program to the test server and storing it for each test taker; A test finishing process in which all test questions are asked and the application program installed in the tablet PC of the test takers is automatically deleted when all test questions are asked, and when the test is finished, the proctor's termination confirmation information is input; It consists of a test presentation process in which the answers written by each test taker received and stored in the test server are compared with the scoring answer information and scored, and the scoring result information is announced.

Appointment of proctors is selected from pre-registered proctors, and these proctors are pre-contracted proctors and are registered and managed in the test information database.

When a supervisor is appointed, the supervisor appointment information is provided and the appointment confirmation information is received from the supervisor to complete the supervisor setting process.

Proctor appointment information includes test location, test time, and number of test takers.

The number of test takers information includes personal information including each test taker's photo, name, gender, and address, and contact information.

The test setting process is a process to check the parts necessary for the test before the pre-set preparation time on the day of the test and to set the device for the test.

When the test center inspects the test takers, the supervisor checks the test taker information and face picture, and enters the test site setting information, which is the confirmation information, the test server provides the test program (App) necessary for the test, and the test taker can download the test program to the tablet It is installed on a PC, transmits and receives data through the test program, and completes the test access confirmation of test takers by inputting the identification information that the test takers entered when they entered the application information.

The test setting information of the proctor includes information on the number of test takers who participated in the test, and the identification information of test takers consists of any one of personal information such as name, resident registration number, or member ID entered by test takers when entering application information.

The test program (App) consists of a process of accessing the system and inputting identification information to download and install the test program, or when the supervisor inputs test setting information in the system, the supervisor It may include a process of providing a path and allowing test takers to install the test program according to the path.

3A is a view showing the concept of an artificial intelligence platform for recognizing facial contours having a tablet PC, a smartphone, and a PC-based test online test/UBT test server during the language, health and medical education online test/UBT test.

Figure 3b is a diagram showing the face recognition function on the ubiquitous-based learning (UBL) and ubiquitous-based testing (UBT) NSDAI platform.

3c and 3d show the goals and User Experience (1. User Registration->2. Learning-> 3. QR Codes of UBI cloud App and NS facere platform used in tablet PCs, smartphones, and PCs during the online test/UBT test) Create -> 4. UBT App/Web login (QR code/passwd, ID/passwd) -> 5. Supervisor confirmation and test-> 6.7 Recognition of facial contours of face of UBT App/Web camera 8. End of test), Fig. 3e is a diagram showing the UI design.

Figure 4 is a diagram showing the process of 1) candidate registration, 2) learning, 3) QR code issuance, 4) QR code and face recognition, 5) supervisor confirmation (respondent face/candidate information), 6) test taking - process.

5 is a screen of candidate registration/learning/QR code issuance during the online test/UBT test.

6 is a screen in which similarity is measured through AI-based facial contour recognition of a test taker's face.

7 is a program test screen.

8 shows test server access/login/left-QR code recognition/QR code recognition during online test or UBT test, greeting sound (TTS)/right-face recognition (smartphone/tablet PC front camera)/face in the background This is a demonstration screen including the area recognition start/recognition rate display process.

(1) The test taker terminal is equipped with a proxy test prevention method that combines an artificial intelligence facial recognition module and a cheating prevention module using a 5-point scale and preventing cheating during the exam

The AI that has learned the candidate registration photo generates a recognition code (QR code) for each user, and the test taker shines the exam ticket with the identification code (QR code) attached to the camera of the exam room PC/tablet PC, and then the AI recognized by the user By comparing the test taker information and the result model learned from the photo, the similarity of the feature points of face recognition is calculated. In the case of not reaching a certain level (completely underachieving, withholding judgment), the test is conducted after passing the information to the supervisor's device and confirming the supervisor's personal information. During the test, the artificial intelligence of the face recognition module or the facial contour / 5 point scale recognition anti-fraud module allows the program to exist in the background or on another layer under the test screen or at a different coordinate value location with the test screen, and a camera attached or built-in to the device Recognizes the user until the end of the test using Information is saved as an image or text/numeric and transmitted to the test server after the test is completed to support the decision of the test supervisor/in charge of the test as to whether or not to take a proxy test/cheating. If a warning is given to the test taker's misconduct, it is provided in the form of a warning message or an alarm to the test supervisor or person in charge, and notified in various ways, such as SMS transmission.

This can prevent cheating, surrogate exams, and reduce the number of exam proctors, dramatically lowering exam costs while ensuring minimal test reliability even in proctor-less online evaluations.

<System Configuration>

1) When using online: User program (PC/smartphone/tablet) > Wired/wireless communication network (WAN, LTE 4G/5G) and internal network (LAN, Wi-Fi) > Server (exam information/testee information) and recognition information Check the necessary information by exchanging, and if true, True/False is returned, and the user program executes the specified action according to the returned value. The candidate's recognition information and results are also transmitted to the supervisor's program (PC/smartphone/tablet). (Sent by program or via SNS/SMS/E-mail)

2) Offline use: The AI learning module is installed in the user program (PC/cell phone/tablet), and only the final recognition result is delivered to the test server after recognizing the facial contour of the test taker's face. When the final recognition result is transmitted from the test taker terminal to the test server, the corresponding information is displayed on a server program (dashboard), and the information is transmitted to the supervisor or the test manager as the corresponding information program or SNS/SMS/e-mail is transmitted.

<Part of the actual app prototype in development APK - Attached for Android>

https://we.tl/t-wFdXexsors file download password: nsdevil

You can directly check some of the above functions by installing the app on your Android smartphone or tablet PC.

<Web version feature address>

1) Face Recognition Module: https://facere.nsdai.org (id: nsdevil, passwd: nsdevil) > Take a QR code with your smartphone and place it on the recognition screen on your PC camera to operate.

2) 5-point scale of facial contour line anti-fraud module- Facial contour recognition module: https://headpos.ublrandd.com.np (id: nsdevil, passwd: nsdevil)

AI-based facial contour recognition technology (posenet algorithm) to prevent proxy tests or cheating in tablet PC, smartphone, and PC-based online tests and UBT tests equipped with artificial intelligence facial recognition module and 5-point scale anti-fraud module Recognizes the contour of the test taker's face and the eye2/nose/ear2 feature points on the test taker terminal using Detects facial behavior patterns, when facial contours cannot be recognized during face recognition, when the tablet PC's camera image deviates from the test screen where it is taken If this value is exceeded), a warning message or alarm is printed or stored in the information device of the test taker's terminal and transmitted to the test server when the test is finished, and the scoring results are provided to test takers, and substitute test or UBT test Prevent cheating.

Embodiments according to the present invention may be implemented in the form of program instructions that can be executed by various computer means and recorded in a computer-readable recording medium. The computer-readable recording medium may include program instructions, data files, and data structures alone or in combination. Computer-readable recording media include storage, hard disks, magnetic media such as floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floppy disks. A hardware device configured to store and execute program instructions in a magneto-optical media, and a storage medium such as ROM, RAM, flash memory, and the like may be included. Examples of program instructions may include those created by a compiler and not only machine language code but also high-level language code that can be executed by a computer using an interpreter. The hardware device may be configured to operate as one or more software modules to perform the operations of the present invention.

As described above, the method of the present invention is implemented as a program and is readable using computer software in the form of a recording medium (CD-ROM, RAM, ROM, memory card, hard disk, magneto-optical disk, storage device, etc.) ) can be stored in

Although described with reference to a specific embodiment of the present invention, the present invention is not limited only to the same configuration and operation as the specific embodiment in order to illustrate the technical idea as described above, and within the limit that does not depart from the technical spirit and scope of the present invention. It can be implemented with various modifications. Accordingly, such modifications should be considered to fall within the scope of the present invention, and the scope of the present invention should be determined by the following claims.

100: Online exam or UBT exam server
101: WWW server 102: control unit
103: member registration 104: user authentication unit
105: test notification unit 106: QR code management unit
107: Greeting Provider (TTS) 108: Supervisor Confirmation Unit
109: face recognition unit 110: cheating processing unit
111: test management unit 120: test information DB
121: candidate DB 123: face DB
200: supervisor terminal 300,310,311: test taker terminal

Claims (13)

an online test or UBT test server that registers test taker information and photos, issues a QR code, and provides a test program for conducting an online test or UBT test;
a test taker terminal connected to the online test or UBT test server through a wired/wireless communication network, and having an artificial intelligence facial recognition module and a 5-point scale cheating prevention module; and
a supervisor terminal connected to the online test or UBT test server through a wired/wireless communication network;
UBT system using facial contour recognition artificial intelligence, including According to claim 1,
The test taker terminal uses any one of a tablet PC, a smartphone, and a PC in which the test program (App) is installed, and includes the artificial intelligence facial recognition module and the eye/nose/ear 5-point scale anti-fraud module, and the UBT system using facial contour recognition artificial intelligence, in which the test program is installed. 3. The method of claim 2,
The AI-based facial contour recognition technology used in the facial recognition module uses a posenet algorithm, a UBT system using facial contour recognition artificial intelligence. According to claim 1,
The test taker terminal uses a facial contour recognition technology (posenet algorithm) for the camera image by the face recognition module and the cheating prevention module, and moves the head to the right/left according to whether the 3 eyes/nose points get closer to the 2 points on both ends of the ears Detects and detects abnormal behavior patterns of the face, and when facial contours are not recognized during face recognition, when the test screen deviated A UBT system using facial contour recognition artificial intelligence that outputs a warning message or alarm to prevent cheating. According to claim 1,
The online test or UBT test server
a WWW server connected to the test taker terminal and the supervisor terminal through wired/wireless communication;
a controller for controlling to provide online or ubiquitous-based learning (UBL) and testing (UBT) to the test taker terminal and the proctor terminal through a wired/wireless communication network;
a member registration unit connected to the control unit, receiving member information of test takers, storing and managing ID/Paaswd;
a user authentication unit connected to the control unit and authenticating a user using a QR code/Passwd or ID/Passwd;
a test notification unit connected to the control unit and providing a schedule and location of a tablet PC, a smartphone, a PC-based online learning and test or ubiquitous-based online learning (UBL) and a test (UBT);
a QR code management unit connected to the control unit, issuing a QR code corresponding to test taker information to test takers, and managing the QR code;
It is connected to the control unit, receives a photograph of the candidate from the camera from the candidate terminal, compares the candidate information stored in the database with the photograph of the candidate, and the supervisor confirms whether the examination is a proxy test and confirms the qualification to take the examination wealth;
a face recognition unit connected to the control unit, for recognizing the outline of the test taker's face and eye2/nose/ear2 feature points using the face recognition module in the test taker terminal, and receiving a face recognition result from the test taker terminal;
Using the facial contour recognition technology (posenet algorithm) for the camera image in the test taker terminal, the head movement to the right/left is detected and abnormal behavior patterns of the face are detected according to whether the 3 eyes/nose points are close to the 2 points of the ears at both ends. A warning message to prevent cheating if the facial contour line cannot be recognized during face recognition, or if it deviates from the test screen (if the release direction exceeds a certain threshold depending on whether the 3 eyes/nose points are closer to the 2 points at both ends) Alternatively, when an alarm is output, the cheating processing unit receives a warning message or an alarm from the test taker terminal;
Provides a test program (App) and test papers to the test taker terminal and the proctor terminal, manages test taker information, on-site face photos, and proctor information a test management unit that stores and manages test takers' written answers, grading results, supervisor information and test taker status information, which stores and manages test taker's test paper answers and test taker status information; and
Test information DB for storing test takers' test papers, written answers, and scoring results; a test taker DB and a face DB for storing test taker information and a standard-sized frontal face picture;
UBT system using facial contour recognition artificial intelligence, including 6. The method of claim 5,
The online test or UBT test server
UBT system using facial contour recognition artificial intelligence further comprising a greeting providing unit (TTS) that provides a greeting through TTS conversion technology after recognizing the QR code. According to claim 1,
The test paper questions of the test program (App) are UBT using facial contour recognition artificial intelligence, in which multimedia test questions including text, images, VR/AR contents, voice and video as well as text and images are presented and displayed for each question. system. Registering member information, logging in/user authentication, registering test taker information and photos to an online test or UBT test server and storing;
issuing, by the online test or UBT test server, a QR code corresponding to candidate information and a photo;
announcing, by the online test or UBT test server, a test schedule and location according to the host test;
In the test taker terminal equipped with a face recognition module using AI-based facial contour recognition technology, the face recognition result of the front photo of the camera is received from the test server, and the proctor terminal compares the test taker information with the photo to confirm whether the test taker is the test taker. and deciding whether to take the exam or not;
The facial recognition module of the test taker terminal and the 5-point scale anti-fraud module recognize the outline of the test taker's face and the eye2/nose/ear2 feature points, and the right / Detects head movement to the left and detects abnormal behavior patterns on the face. receiving a warning message or alarm from the candidate terminal when a warning message or alarm is output to prevent cheating); and
The test program (App) and the online test paper are provided to the test taker terminal and the proctor terminal, and the test taker information, the on-site face photo of the test taker, and the proctor information are stored and managed in the database of the test server. storing the test paper written answers in each test taker terminal for a predetermined test time, receiving and storing the test paper answers from the test taker terminal at the end of the test, and storing, and providing the scoring results of the test takers' written answers to the test taker terminal;
A method of providing UBT using artificial intelligence for facial contour recognition comprising a. 9. The method of claim 8,
The test taker terminal uses any one of a tablet PC, a smartphone, and a PC in which the test program (App) is installed, and includes the artificial intelligence facial recognition module and the eye/nose/ear 5-point scale anti-fraud module, and the A method of providing UBT using facial contour recognition artificial intelligence, in which a test program is installed. 10. The method of claim 9,
The AI-based facial contour recognition technology used in the facial recognition module uses a posenet algorithm, a UBT providing method using facial contour recognition artificial intelligence. 9. The method of claim 8,
The test taker terminal uses a facial contour recognition technology (posenet algorithm) for the camera image by the face recognition module and the cheating prevention module, and moves the head to the right/left according to whether the 3 eyes/nose points get closer to the 2 points on both ends of the ears Detects and detects abnormal behavior patterns of the face, and when facial contours are not recognized during face recognition, when the test screen deviated A method of providing UBT using facial contour recognition artificial intelligence, in which a warning message or alarm is output to prevent cheating. 9. The method of claim 8,
It operates by taking the QR code with a smartphone and touching the recognition screen to the camera of the test taker terminal, and after recognizing the QR code, providing a greeting through a Text To Speech (TTS) function. The UBT delivery method used. 9. The method of claim 8,
The test paper questions of the test program (App) provide UBT using facial contour recognition artificial intelligence, in which multimedia test questions including text, images, VR/AR contents, voice and video as well as text and images are presented for each question and displayed. Way.

Images