Real-Time Computer Vision: Face Recognition with a Robot
We, the members of the computer vision working group at statworx, had set ourselves the goal of building more competencies in our field with the help of exciting projects. For this year’s statworx Alumni Night, which took place at the beginning of September, we came up with the idea of a greeting robot that would welcome the arriving employees and alumni of our company with a personal message. To realize this project, we planned to develop a facial recognition model on a Waveshare JetBot. The Jetbot is powered by an NVIDIA Jetson Nano, a small, powerful computer with a 128-core GPU for fast execution of advanced AI algorithms. Many popular AI frameworks such as Tensorflow, PyTorch, Caffe, and Keras are supported. The project seemed to be a good opportunity for both experienced and inexperienced members of our working group to build knowledge in computer vision and gain experience in robotics.
Face recognition using the JetBot required solving several interrelated problems:
- Face Detection: Where is the face located in the image?
First, the face must be located on the provided image. Only this part of the image is relevant for all subsequent steps. - Face embedding: What are the unique features of the face?
Next, unique features must be identified and encoded in an embedding that is used to distinguish it from other people. An associated challenge is for the model to learn how to deal with slopes of the face and poor lighting. Therefore, before creating the embedding, the pose of the face must be determined and corrected so that the face is centered. - Name determination: Which embedding is closest to the recognized face?
Finally, the embedding of the face must be compared with the embeddings of all the people the model already knows to determine the name of the person. - UI with welcome message
We developed a UI for the welcome message. For this, we had to create a mapping beforehand that maps the name to a welcome message. - Configuration of the JetBot
The last step was to configure the JetBot and transfer the model to the JetBot.
Training such a face recognition model is very computationally intensive, as millions of images of thousands of different people are used to obtain a powerful neural network. However, once the model is trained, it can generate embeddings for any face (known or unknown). Therefore, we were fortunately able to use existing face recognition models and only had to create embeddings from the faces of our colleagues and alumni. Nevertheless, I’ll briefly explain the training steps to give an understanding of how these face recognition models work.
We used the Python package face_recognition, which wraps the face recognition functionality of dlib to make it easier to work with. Its neural network was trained on a dataset of about 3 million images and achieved 99.38% accuracy on the Labeled Faces in the Wild (LFW) dataset, outperforming other models.
From an image to the encoded face (Face Detection)
The localization of a face on an image is done by the Histogram of Oriented Gradients (HOG) algorithm. In this process, each individual pixel of the image is compared with the pixels in the immediate vicinity and replaced by an arrow pointing in the direction in which the image becomes darker. These arrows represent the gradients and show the progression from light to dark across the entire image. To create a clearer structure, the arrows are aggregated at a higher level. The image (a) is divided into small squares of 16×16 pixels each and replaced with the arrow direction that occurs most frequently (b). Using the HOG-encoded version of the image, the image sector that is closest to a HOG-encoded face (c) can now be found. Only this part of the image is relevant for the following steps.
Figure 1: Turning an image to a HOG pattern
source: https://commons.wikimedia.org/wiki/File:Dlib_Learned-HOG-Detector.jpg
Making faces learnable with embeddings
For the face recognition model to be able to assign different images of a person to the same face despite some images being tilted or suffering from poor lighting, the pose of the face needs to be determined and used for projection so that the eyes and lips are always in the same position of the resulting image. This is done using the Face Landmark Estimation algorithm, which can find specific landmarks in a face using machine learning. This allows us to locate the eyes and mouth and adjust them using basic image transformations such as rotation and scaling, so that the facial features are as centered as possible.
Figure 2: Face landmarks (left) and projection of the face (right)
In the next step, a neural network is used to create an embedding of the centered face image. The neural network learns meaningful embeddings by looking at three face images simultaneously within one training step: Two images of a known person and one image of another person. The neural network creates the embeddings of the three images and optimizes its weights so that the embeddings of the images of the same person are approximated and differ more with the embedding of the other person. After repeating this step millions of times for different images of different people, the neural network learns to generate representative embeddings. The network architecture of dlib‘s face recognition model is based on ResNet-34 from the paper Deep Residual Learning for Image Recognition by He et al., with fewer layers and several filters reduced by half. The generated embeddings are 128-dimensional.
Matching faces with learned embeddings in real time
Fortunately, we could use the existing model from dlib for our robot. For the model to recognize the faces of the current and former statcrew, we only had to create embeddings of everyone’s faces. For this we used the official statworx portraits and saved the resulting embedding together with each respective name. If an unknown image is then fed into the model, the face is detected, centered and turned into an embedding. The created embedding is then matched with the stored embeddings of the known people and if there is a high similarity, the name of this person is returned. If there is no similar embedding, there is no match with the knows faces. More images of a person and thus multiple embeddings per person would improve the performance of the model. However, our tests showed that our faces were recognized quite reliably even with only one image per person. After this step, we now had a good model that recognized faces in the camera in real time and displayed the associated name.
A warm welcome via a UI
Our plan was to equip the robot with a small screen or speaker so the welcome message could be seen or heard. As a starting point, we settled on connecting the robot to a monitor and building a UI to display the message. First, we developed a simple UI which displayed the welcome message in front of a background with the statworx company values and projected the camera image in the lower right corner. For each person to receive a personalized message, we had to create a json file that defined the mapping from the names to the welcome message. For unfamiliar faces, we had created the welcome message “Welcome Stranger!”. Because of the many namesakes at statworx, everyone with the name Alex also received a unique identifier:
Last hurdles before commissioning the robot
Since the model and the UI were only running locally so far, there was still the task of transferring the model to the robot with its integrated camera. Unfortunately, we found out that this was more complicated than expected. We were faced with memory problems time and again, meaning that we had to reconfigure the robot a total of three times before we could successfully deploy the model. The instructions for configuring the robot, which helped us succeed in the end, can be found here: https://jetbot.org/master/software_setup/sd_card.html. The memory problems could mostly be solved with a reboot.
The use of the welcome robot at the Alumni Night
Our welcome robot was a great success at the Alumni Night! Our guests were very surprised and happy about the personalized message.
Figure 3: Welcoming guests to the statworx-Alumni-Night
The project was also a big success for the Computer Vision cluster. During the project, we learned a lot about face recognition models and all the challenges associated with them. Working with the JetBot was particularly exciting and we are already planning more projects with the robot for next year.
