Artificial intelligence has been solving the real-life problems for decades. Artificial intelligence has now grown up to a technology that impacts every major industry. Many of the artificial intelligence applications require the use of computer vision technology to process a large amount of data.
Face detection and tracking can be used for used for security purposes, detecting potential threats and creating the systems that can act as the robotic eye for real-time detection. Some of the applications that are using computer vision include:
Facebook has developed an algorithm that can recognize your friends in the photographs. In earlier days, Facebook used to ask the user to type their name and tag them. This technology is known as face recognition technology. Facebook’s algorithm is able to recognize your friend’s face after they have been tagged multiple times.
How AI can help in Face Detection
Face recognition is a collection of several related problems:
A human brain can perform these tasks automatically and instantly. On the other hand, computers are not able to generalize like we do. So, they need to be trained in each of the processes separately. A separate pipeline is to be made to solve each and every step and to move to the next level. In other words, we need to chain several machine learning algorithms to find a face in the image, analyze the facial features, compare against known faces, and make a prediction.
Step by step approach to face recognition
In this article, we will tackle the problem one by one. For every step, we will be learning about different machine learning algorithms.
Step 1: Finding the Faces
The step in the pipeline is the face detection. We need to locate the faces in the photograph before we can actually try to separate them. Face detection is a great feature in the cameras. When the camera automatically picks our faces, it can make sure that all the faces are in focus before it takes a picture. To find the faces in the image, first, we have to make the image black and white because we do not needs colored faces to find the faces.
Then, we look at every single pixel in our image one at a time. Look at the pixels that directly surrounds it. We have to figure out how dark the current pixel is as compared to the pixels that surround. Repeat the process with every single pixel in the image and you will end up with replacing every pixel being replaced by an arrow. The end result is that we turn the image into a simple representation that captures the basic structure of the face.
Step 2: Posing and Projecting Faces
Next, we have to deal with the problem of posing and projecting faces. For this, we try to wrap each picture so that the lips are always in the sample place in the image. This will make a lot easier for us to compare the faces. The basic idea is to find the 68 specific points that exist on every face. Then a machine learning algorithm is trained to find the specific points on the face. Now, that we have located the eyes and mouth, we will simply rotate and scale the images so that the eyes and mouth are centered at the best possible position. This will make our next step a lot easier.
Step 3: Encoding the Faces
In this step, we have to tell the faces apart. The simplest approach is to directly compare the unknown faces that we found out in Step 2 with the images of the people that have already been tagged. When we find a previously tagged face that is very similar to the untagged face, it must be the same person.
The major problem with this approach is that the system cannot loop through the millions of people in the database. It can take time in minutes. However, the results we require should be in milliseconds. What we can do is, we can extract the few basic measurements from each face.
Then, we can measure the unknown face in the same way and find the known face with the closest measurement. For example, the spacing between the ears, eyes, length of nose etc. The Deep Convolutional Neural Network can be used to train and generate 128 measurements of each face.
Encoding the Face:
The process of training a convolutional neural network requires a lot of data and computer power. But once the network has been trained, it can generate measurement for any face. The last step in the process is to find out the person in the database who has the closest resemblance with the test image.
This can be achieved by a simple machine learning classification algorithm. All we need to do is to train a classifier that can take a measurement from the test image and tell which person is the closest match. Running the classifier takes milliseconds. The result of the classifier is the name of the person.
In this article, we discussed how face detection and recognition work with artificial intelligence. Recent advancements in the artificial intelligence have significantly improved the accuracy of the facial recognition. Deep learning has significantly helped in the field of computer vision technology and natural language processing. Webtinix AI is an artificial intelligence company in India that has built facial detection and recognition systems that can be used in various real-time applications.