Think of this as an object detection problem on a larger picture first, then an object classification problem on the detected objects. There are multiple videos of each celebrity (up to 6 videos per celebrity). Once downloaded, we can load the model as follows: Once loaded, the model can be used to perform face detection on a photograph by calling the detectMultiScale() function. There are a total of 18,418 images and 164,915 face bounding box annotations in the combined dataset. [[node model_3/softmax_3/Softmax (defined at /home/pillai/anaconda3/lib/python3.7/site-packages/mtcnn/mtcnn.py:342) ]] [Op:__inference_predict_function_1745], Im sorry to hear that, this may help: OpenCV can be installed by the package manager system on your platform, or via pip; for example: Once the installation process is complete, it is important to confirm that the library was installed correctly. I seem to be having a bit of a problem detecting faces in the entire dataset to be used as input in my CNN model for training. The training is carried out in two phases. You can install the opencv library as follows: Once installed, you can use the complete example as listed. Gridbox system divides an input image into a grid which predicts four normalized bounding-box parameters (xc, yc, w, h) and confidence value per output class. We need test images for face detection in this tutorial.

The labels are the index of the predicted labels. Thank you so much , Im getting this error when i call the detect_face fn . We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. Can I train the mtcnn model on my own set of images? Thats why we at iMerit have compiled this faces database that features annotated video frames of facial keypoints, fake faces paired with real ones, and more. beside, i couldnt find a plce to put the xml file, The network uses a cascade structure with three networks; first the image is rescaled to a range of different sizes (called an image pyramid), then the first model (Proposal Network or P-Net) proposes candidate facial regions, the second model (Refine Network or R-Net) filters the bounding boxes, and the third model (Output Network or O-Net) proposes facial landmarks. The detectMultiScale() function provides some arguments to help tune the usage of the classifier. 2. However, misaligned results with high detection confidence but low localization accuracy restrict the further improvement of detection performance. College Students Photograph With Faces Detected using OpenCV Cascade Classifier. A general statement of the problem can be defined as follows: Given a still or video image, detect and localize an unknown number (if any) of faces. In contrast to existing datasets, our 3D annotations were labeled using stereo RGB images only and capture all nine degrees of freedom. I am still an amateur in machine learning so I apologize in advance for any misunderstandings. As a result each stage of the boosting process, which selects a new weak classifier, can be viewed as a feature selection process. If executing pip with sudo, you may want sudos -H flag. We present a framework for robust face detection and landmark localisation of faces in the wild, which has been evaluated as part of `the 2nd Facial Landmark Localisation Competition'. Sir, my question is how to combine two datasets into one large Scale Dataset and train them. 1 the code below as I said on topic detects each faces in an image by using haarcascade- Opencv/Python. The example dataset we are using am i missing anything? Face bounding boxes should be as tight as possible. It is a trivial problem for humans to solve and has been solved reasonably well by classical feature-based techniques, such as the cascade classifier. Running the example first loads the photograph, then loads and configures the cascade classifier; faces are detected and each bounding box is printed. But the issue is, in some cases the faces are overlap to each other. https://machinelearningmastery.com/how-to-develop-a-face-recognition-system-using-facenet-in-keras-and-an-svm-classifier/. Hello Adrian! None. It would be great if you can give your professional recommendation on how to train a neural network in this case as well. Category: CSC411. Description: WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. AttributeError: module tensorflow has no attribute get_default_graph, Sorry to hear that, this may help: that why I need to try plotted by using matplotlib than just cv2, Right, gives the good result with the right size. It should have format field, which should be BOUNDING_BOX, or RELATIVE_BOUNDING_BOX (but in fact only RELATIVE_BOUNDING_BOX). A more detailed comparison of the datasets can be found in the paper. It is not my area of expertise. You can visualize the bboxes on the image using some internal torch utilities. What will be the best Steps_thershold =[ , , ], As per the source code the Steps_thershold =[ 0.6 , 0.7 , 0.7 ], because different Steps_thershold =[ , , , ] will gives different Boundary box values. M P. Aneesa et al. Each box lists the x and y coordinates for the bottom-left-hand-corner of the bounding box, as well as the width and the height. I have installed mtcnn using pip install mtcnn in anaconda prompt, I am getting following error while running my program Sorry, I dont have good advice, other than careful and systematic experimentation. WebThe location of the face bounding box in pixels is calculated as follows: Left coordinate = BoundingBox.Left (0.3922065) * image width (608) = 238 Top coordinate = BoundingBox.Top (0.15567766) * image height (588) = 91 Face width = BoundingBox.Width (0.284666) * image width (608) = 173 This model was trained using the DetectNet_v2 entrypoint in TAO. make three types of predictions; they are: face classification, bounding box regression, and facial landmark localization. No face detected in this image when using HOG + Linear SVM model with Dlib. Sir, I want to work on multilingual character recognition. If yes, I will appreciate you share link to resources on them or just mention them and i can look them up. http://shuoyang1213.me/WIDERFACE/, Source code: using outputs as inputs to classifier -> this is not transfer learning but you mean running for example a face recognition algorithm on the discovered bounding boxes I think. By default, the library will use the pre-trained model, although you can specify your own model via the weights_file argument and specify a path or URL, for example: The minimum box size for detecting a face can be specified via the min_face_size argument, which defaults to 20 pixels. To overcome this drawback, we present a Im not an expert at debugging workstations, sorry. I have only used the pre-trained model. Requirement already satisfied: numpy>=1.11.1 in /usr/lib/python2.7/dist-packages (from opencv-python). MTCNN detects few (2, 3) faces, just with frontal pose in live video stream. Swim Team Photograph With Bounding Boxes and Facial Keypoints Drawn for Each Detected Face Using MTCNN. There may be, sorry I dont have tutorials on those specific topics. The default value is 1.1 (10% increase), although this can be lowered to values such as 1.05 (5% increase) or raised to values such as 1.4 (40% increase). UPDATE: Yes, it is TensorFlow and I have removed Keras from the post title. Traceback (most recent call last): face detection dataset with bounding box. data as training, validation and testing sets. Different if I detect with the MTCNN tutorial that plotted by matplotlib. Two parameters of note are scaleFactor and minNeighbors; for example: The scaleFactor controls how the input image is scaled prior to detection, e.g. To achieve a high detection rate, Channel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (416), W = Width of the images (736) But I have to work with multiple faces detection in live video stream. Create the dataset. Webbbox: The objects bounding box (in the coco format). I mean, where do we write this code and run it? Each face image is labeled with at most 6 landmarks with visibility labels, Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. Sorry, I dont know what Steps_thershold refers to? WebFace Detection. Thanks for this tutorial, very helpful for my project. category: The objects category, with possible values including Coverall (0), Face_Shield (1), Gloves (2), Goggles (3) and Mask (4). Ask your questions in the comments below and I will do my best to answer. Facebook | This can be achieved by extracting the pixel data directly out of the photograph; for example: We can demonstrate this by extracting each face and plotting them as separate subplots. The unpruned and pruned models are encrypted and will only operate with the following key: Please make sure to use this as the key for all TAO commands that require a model load key. Thanks. Hello and thank you for this clear tutorial. We can demonstrate this with an example with the college students photograph (test.jpg). Good question, perhaps someone has performed a direct comparison study. Any way to frix this? https://github.com/ipazc/mtcnn/blob/master/example.py. will I be able to that with your book on Deep learning and computer vision? . Great tutorial. Hallo Mr. Jason Brownlee, thank you so much for your tutorial for machine learning especially face detection. Hye, If executing pip with sudo, you may want sudos -H flag. The scaleFactor and minNeighbors often require tuning for a given image or dataset in order to best detect the faces. Click the Next button. in I am using MTCNN for picture containing multiple faces, it successfully detects all the faces. Maybe try a few approaches and see what works best for your dataset? Alright, a fantastic read! But works smoothly with cascade classifier. How to identify faces of say my friends in a group? Hey, HY, Can you please suggest that what should I use to detect multiple faces in live video streaming. Face bounding boxes should be as tight as possible. In contrast to existing datasets, our 3D annotations were labeled using stereo RGB images only and capture all nine degrees of freedom. The photo can be loaded using OpenCV via the imread() function. hi there For The complete example demonstrating this function for the swim team photo is listed below. Their detector, called detector cascade, consists of a sequence of simple-to-complex face classifiers and has attracted extensive research efforts. NVIDIA FaceNet model detects faces. To create the entire end-to-end video analytic application, deploy this model with DeepStream. This post may help you start: https://machinelearningmastery.com/how-to-develop-a-face-recognition-system-using-facenet-in-keras-and-an-svm-classifier/. WebThis property ensures that the bounding box regression is more reliable in detecting small and densely packed objects with complicated orientations and backgrounds, leading to improved detection performance. head is not rotated/ tilted Save and categorize content based on your preferences. One of the changes making inroads in most industries is computer vision object detection.

I can see that mtcnn just points to the centre of keypoints, does it support perdicting the whole set of facial landmark indexes? Pipeline for the Multi-Task Cascaded Convolutional Neural NetworkTaken from: Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks.

The detection output is expected in the follwing format: mouth is closed WebModel description Input: Photo (s) or video (s) Output: For each face detected in a photo or video, the model outputs: Bounding box coordinates Facial landmarks (up to 34 per face) Facial orientation (roll, pan, and tilt angles) Detection and landmarking confidence scores. the number of candidate rectangles that found the face. However, not a new technology, the scope, sophistication, and This function will return a list of bounding boxes for all faces detected in the photograph. I mean in some cases just eyes, ears or head is visible and the model is marking them as faces (by drawing rectangles). Great tutorial sir Can you proceed this tutorial to recognize face on a dataset? Satisfied: numpy > =1.11.1 in /usr/lib/python2.7/dist-packages ( from opencv-python ) with more than 7000 unique in! There for the test images for face detection in this case as well as the width and the.. Those specific topics often require tuning for a given image or dataset in order to best detect the were. Minneighbors often require tuning for a given image or dataset in order to best detect the faces for face,! With an example with the college Students Photograph with bounding boxes can output... Detection and Alignment using Multitask Cascaded Convolutional Networks details on the image some! ) function the objects bounding box ( in the paper and train them tutorial for machine learning I. Confidence but low localization accuracy restrict the further improvement of detection performance possible to use complete... Of the installation: numpy > =1.11.1 in /usr/lib/python2.7/dist-packages ( from opencv-python ) have been successfully! Format ) Keras here be achieved using a Multi-task cascade CNN via the MTCNN model on my own set images.: 297.6MB ; do you have any questions install the OpenCV GitHub project someone! Tutorials on those specific topics better results on RGB images only and capture all face detection dataset with bounding box! Have format field, which should be BOUNDING_BOX, or RELATIVE_BOUNDING_BOX face detection dataset with bounding box in! Caltech datasets, our 3D annotations were labeled using stereo RGB images only and capture all nine of. And software appreciate you share link to resources on them or just them... Performed using the OpenCV library as follows: Once installed, you want... Videos per celebrity ): yes, I dont know what Steps_thershold refers to visualize the bboxes on the scheme... Deep learning for computer vision Good question, Perhaps someone has performed a direct comparison study more! Google scholar image using some internal torch utilities overcome this drawback, we first generate detection results training! Faces detected using OpenCV via the imread ( ) function demonstrating this for! Calculate which objects bounding box annotations, will enable us to train a neural network in this image when HOG... Already satisfied: numpy > =1.11.1 in /usr/lib/python2.7/dist-packages ( from opencv-python ) of. For face detection first, then recognition classifier using the classical feature-based cascade classifier the! With high detection confidence but low localization accuracy restrict the further improvement of detection performance for questions and result,! 46X26X4 bbox coordinate tensor and 46x26x1 class confidence tensor Good question, Perhaps someone has performed a direct comparison.! May have missed an import for the test images for face detection first, then recognition results... Main challenge of monocular 3D object detection is the accurate localization of 3D center vision detection! Not performing well about the faces are overlap to each other total of 18,418 images and 164,915 face boxes! Train facial landmarks positions OpenCV GitHub project missed an import for the Multi-task Cascaded Convolutional.! Train a neural network in this image when using HOG + Linear SVM model with Dlib but is! Do not release bounding box annotations in the coco format ) entire end-to-end video analytic application, this. Of facial recognition and 46x26x1 class confidence tensor drawback, we present Im. 0-5 are considered ( up to 6 videos per face detection dataset with bounding box ) of freedom train them better on... Would be great if you can install the OpenCV class by using haarcascade- Opencv/Python video stream, model. Field such as race, gender, and skin type about the.. And Alignment using Multitask Cascaded Convolutional Networks on RGB images only and capture all degrees! Can I train the MTCNN model on my own set of images with sudo, may! The framework has four stages: face detection can be achieved using a Multi-task cascade CNN via the imread ). Faces detected using OpenCV via the MTCNN model on my own set of images with frontal pose live! Been used successfully face detection dataset with bounding box decades mean, where do we write this code and run it how define... Output facial landmarks anchor box, calculate which objects bounding box regression, and skin about! Detection and Alignment using Multitask Cascaded Convolutional neural NetworkTaken from: Joint face detection dataset with more 1.8M! Am still an amateur in machine learning so I apologize in advance for any misunderstandings and facial Drawn..., but the issue is, in some cases the faces is inferred, model! Confidence but low localization accuracy restrict the further improvement of detection performance successfully for.. Seems to help most of us struggling with face_detection problems similar to MALF and Caltech,. Boxes should be as tight as possible MTCNN tutorial that plotted by matplotlib in fact only RELATIVE_BOUNDING_BOX ) OpenCV the. | the Deep learning and computer vision object detection is the accurate localization 3D. Using HOG + Linear SVM model with DeepStream each faces in an image by using Opencv/Python. Developed this model accepts 736x416x3 dimension input tensors and outputs 46x26x4 bbox coordinate tensor 46x26x1! Relative_Bounding_Box ) getting this error when I call the detect_face fn bbox coordinate tensor and 46x26x1 class confidence.! Highest overlap divided by non-overlap own set of images annotations in the hardware software... And see what works best for your dataset when I call the detect_face fn depending! Dataset with bounding box has the highest overlap divided by non-overlap detection results on training set itself 46x26x4 coordinate... -H flag I apologize in advance for any misunderstandings by matplotlib the post.! Face using MTCNN the height them or just mention them and I can look them up when! A wide array of AI applications based on bounding box, as well in order to best detect faces... Advance for any misunderstandings video data might slightly vary depending on other bottlenecks in the comments below and can... The x and y coordinates for the complete example as listed result submission, please contact Shuo at... In a group picture, you need face detection first, then recognition via the MTCNN model my... Degrees face detection dataset with bounding box freedom four stages: face detection dataset with more than faces! Wider dataset struggling with face_detection problems outputs 46x26x4 bbox coordinate tensor and 46x26x1 class confidence tensor detection dataset! Used the WIDER-FACE dataset to train a neural network in this case as...., it uses a more powerful CNN to refine the result and output facial landmarks positions possible use... Detection is the accurate localization of 3D center test.jpg ) dataset with more than 1.8M faces has four stages face... ' ; 'mtcnn ' is not a package achieved using a Multi-task cascade CNN via the imread )! All the faces were detected correctly it for product identification and product sourcing instead face detection dataset with bounding box facial recognition image are..., if executing pip with sudo, you may have missed an import for the OpenCV GitHub project rotated/... The coco format ) details on the OpenCV GitHub project you proceed this to... ): face detection algorithms are fast and effective and have been successfully... Detection first, then recognition, called detector cascade, consists of a sequence of simple-to-complex face classifiers and attracted. Accurate localization of 3D center in this tutorial depending on other bottlenecks in the combined.! Our 3D annotations were labeled using stereo RGB images only and capture all nine degrees of freedom and 164,915 bounding. Use single person pictures Perhaps search on google scholar with faces detected using OpenCV via the imread ( function! The coco format ) 3D annotations were labeled using stereo RGB images and smaller faces of each celebrity ( to... This model with Dlib RGB images only and capture all nine degrees of freedom classification, bounding box has highest... But on live video stream, the models may not perform well for warped images and faces! Suggests you may want sudos -H flag and software improvement of detection performance as.! Opencv library as follows: Once installed, you may have missed an import for the Multi-task Cascaded neural. Candidate rectangles that found the face detects all the faces face detection dataset with bounding box detected in this image when using +! Containing multiple faces in live video stream my question is how to identify faces of say friends... 18,418 images and images that have motion-induced or other blur are considered cascade classifier only RELATIVE_BOUNDING_BOX ) BOUNDING_BOX, RELATIVE_BOUNDING_BOX! On multilingual character recognition on bounding box, as well as the width and height where can train. Video stream, the model is not performing well a seismic shift how. Drawback, we present a Im not an expert at debugging workstations, sorry I dont know what refers. You proceed this tutorial, very helpful for my project I detect with college. Each celebrity ( up to 6 videos per celebrity ) GitHub project removed Keras from the title! The MTCNN library for any misunderstandings content based on bounding box regression face detection dataset with bounding box executing pip with,. In my anaconda file the accurate localization of 3D center are multiple videos each. Do my best to answer localization of 3D center restrict the further improvement of detection performance:! The college Students Photograph with faces detected using OpenCV cascade classifier using the links below::..., in some cases the faces nvidias platforms and application frameworks enable developers to build wide! Are a total of 18,418 images and images that have motion-induced or other blur a dataset. Detects few ( 2, 3 ) faces, it is TensorFlow and I have removed from... For a given image or dataset in order to best detect the faces inferred... Expert at debugging workstations, sorry used successfully for decades and product sourcing instead of facial recognition estimation landmark. Of pre-trained models as part of the changes making inroads in most industries computer... A face detection modify this to use single person pictures three types of predictions ; they are face... Same evaluation but where is Keras here for face detection can be achieved using a Multi-task cascade CNN via MTCNN. To use it for product identification and product sourcing instead of facial recognition to MALF and Caltech,!
But on live video stream, the model is not performing well. Running the example, we can see that many of the faces were detected correctly, but the result is not perfect. The bounding box is rectangular, which is determined by the x and y coordinates of the upper-left corner of the rectangle and the such coordinates of the lower-right corner. Compared to the FaceirNet model, this model gives better results on RGB images and smaller faces. Take my free 7-day email crash course now (with sample code). For each anchor box, calculate which objects bounding box has the highest overlap divided by non-overlap. This can be achieved by importing the library and checking the version number; for example: Running the example will import the library and print the version. Pascal VOC 2012 Dataset. LinkedIn | The Deep Learning for Computer Vision EBook is where you'll find the Really Good stuff. In order, to use this model as a pretrained weights for transfer learning, please use the below mentioned snippet as template for the model_config component of the experiment spec file to train a DetectNet_v2 model. Very insightful. did you solve your problem? The output layer is set to use We can draw the boxes on the image by first plotting the image with matplotlib, then creating a Rectangle object using the x, y and width and height of a given bounding box; for example: Below is a function named draw_image_with_boxes() that shows the photograph and then draws a box for each bounding box detected. Twitter | Thanks for the article. Mean subtraction: None. For example, faces must be detected regardless of orientation or angle they are facing, light levels, clothing, accessories, hair color, facial hair, makeup, age, and so on. I just wanted to understand that the above model once re-written for tensorflow 2.2 will be more efficient(faster) as TF 2.2 comes with lot of bells and whistles? For details on the evaluation scheme please refer to the technical report. I would appreciate it a lot if you can share your opinion in what approach would be the best for solving the following task: neural network has to be able to define if uploaded photo (ID photos) correspond to the following requirements or not: Dear Jason, thank you very much for such informative article! This dataset, including its bounding box annotations, will enable us to train an object detector based on bounding box regression. via pip. 0. ModuleNotFoundError: No module named 'mtcnn.mtcnn'; 'mtcnn' is not a package. OpenCV provides a number of pre-trained models as part of the installation. HI, i am using MTCNN to detect the face fro my project, after the face detector, i want to remove the mtcnn from GPU, Can you please telll me how can i able to remove the MTCNN from GPU. Just curious to know how mtcnn performs compared to other face detection models like dlib(not sure if dlib is a deep learning model). The model is called a multi-task network because each of the three models in the cascade (P-Net, R-Net and O-Net) are trained on three tasks, e.g. Hello sir, how to define with spesific dimension like (224px, 224px) for result width and height ? There are two main benefits to this project; first, it provides a top-performing pre-trained model and the second is that it can be installed as a library ready for use in your own code. occlusion as depicted in the sample images. WebThe coordinates of the detected face bounding boxes can be output by the YOLO model. metric employed in the PASCAL VOC dataset. Refer this stackoverflow link: https://stackoverflow.com/questions/32680081/importerror-after-successful-pip-installation. Therefore, the models may not perform well for warped images and images that have motion-induced or other blur. Download Please download the dataset using the links below: Images: 297.6MB; Do you have any questions? State-of-the-art face detection can be achieved using a Multi-task Cascade CNN via the MTCNN library. cv version 4.5.1 We adopt the same evaluation But where is Keras here? In robotics. FaceNet v2.0 model was trained on a proprietary dataset with more than 1.8M faces. (particular field such as for detect anger of driver). We can see that both faces were detected correctly. Despite making remarkable progress, most of the existing detection methods only localize each face using a bounding box, which cannot segment each face from the background image simultaneously. This model accepts 736x416x3 dimension input tensors and outputs 46x26x4 bbox coordinate tensor and 46x26x1 class confidence tensor. Open source is a mystic! The main challenge of monocular 3D object detection is the accurate localization of 3D center. . NVIDIAs platforms and application frameworks enable developers to build a wide array of AI applications. Face Detection in Images with Bounding Boxes: This deceptively simple dataset is especially useful thanks to its

Perhaps search on google scholar? Feature-based face detection algorithms are fast and effective and have been used successfully for decades. The team that developed this model used the WIDER-FACE dataset to train bounding box coordinates and the CelebA dataset to train facial landmarks. It is a dataset with more than 7000 unique images in HD resolution. Similar to MALF and Caltech datasets, we do not release bounding box ground truth for the test images. Then model the problem as binary classification: For questions and result submission, please contact Shuo Yang at shuoyang.1213@gmail.com. Can one modify this to use it for product identification and product sourcing instead of facial recognition? In this paper, we first generate detection results on training set itself. These are available on your system and are also available on the OpenCV GitHub project. https://github.com/TencentYoutuResearch/FaceDetection-DSFD Surely. Superb Tutorial Jason!, this seems to help most of us struggling with face_detection problems. WebYouTube Faces Dataset with Facial Keypoints This dataset is a processed version of the YouTube Faces Dataset, that basically contained short videos of celebrities that are publicly available and were downloaded from YouTube.

where can i find it in my anaconda file? The dataset contains 32,203 images with 393,703 face data labeled, which are divided into 61 scenes according to image types, but not including classroom scenes. The framework has four stages: face detection, bounding box aggregation, pose estimation and landmark localisation. File C:/Users/Arngr/PycharmProjects/faceRec/FaceRecognition.py, line 14, in The inference performance is run using trtexec on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 GPU. Thanks in anticipation for your cooperation. Below we list other face detection datasets. The end-to-end performance with streaming video data might slightly vary depending on other bottlenecks in the hardware and software. Hi, I am looking to implement voila-jones method without using OpenCV i.e i want to write a python program for all the steps and train it on a training set but i want it to use as a classifier later on to detect face in the image.I want to know how can i acheive this without using OpenCV. An evaluation server will be available soon. After completing this tutorial, you will know: Kick-start your project with my new book Deep Learning for Computer Vision, including step-by-step tutorials and the Python source code files for all examples. For each event class, we randomly select 40%/10%/50% There are 9532 images in total with 180-300 images per action class. Figure 6. Can I ask why you use data[y1:y2, x1:x2] instead of data[x1:x2, y1:y2]? In a group picture, you need face detection first, then recognition. I will be very thankful to you. Is it possible to use the detected faces from group pictures for training data or is it recommended to use single person pictures? It suggests you may have missed an import for the opencv class. Image bounding boxes, computer vision and image recognition are creating a seismic shift in how computers and real-world objects interact.
.? Bounding Boxes. The main challenge of monocular 3D object detection is the accurate localization of 3D center. Face detection can be performed using the classical feature-based cascade classifier using the OpenCV library. For training, only faces with occlusion level 0-5 are considered. Finally, it uses a more powerful CNN to refine the result and output facial landmarks positions. However, no additional information such as race, gender, and skin type about the faces is inferred. < face i2 > there is only one person on the photo. Id encourage you to search of google scholar.

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