1. Overview
In this tutorial, we’ll discuss how face recognition works in the modern machine-learning era. First, we’ll make an introduction to the area of face recognition, and then we’ll present how deep learning is used for dealing with the task. Then, we’ll talk about some applications and some challenges of face recognition.
2. Introduction
Machine learning has revolutionalized many areas of computer vision due to the high representation capability of neural networks and the huge amount of available training data. One of these areas is face recognition, where our goal is to recognize a person’s identity in a given input image. Using deep learning, face recognition systems improved their accuracy and speed, turning their use in real-time applications into a reality.
3. Deep Learning for Face Recognition
Many proposed deep-learning architectures for facial recognition aim to analyze and identify unique features of a person’s face. Every algorithm consists of the following three stages.
The first step is face detection which aims to detect the part of the input image that contains the person’s face. Once we locate the facial region, a neural network is trained to learn the important features of the face. Usually, the most discriminative parts of a human face are the eyes, the nose, the mouth, and the jawline.
So, after model training, every face can be represented as a vector which is a unique mathematical representation of each face, also known as a faceprint. The faceprint is unique to each individual and can be used to identify them. The diagram below illustrates the training stage:
The final stage is face matching which involves detecting the person’s identity in the given image. To achieve this, we compare the image’s representation with the ones we saved in the face database. The database can contain many images, from passport photos to security camera footage.
The neural network compares the faceprint of the detected face to the faceprints in the database. The system will identify the person associated with that faceprint if there is a match. The diagram below illustrates the face-matching phase:
4. Applications
Now, let’s talk a bit about some of the face recognition applications in various fields.
4.1. Security
The most common application of face recognition is in surveillance systems. More specifically, cameras can monitor public spaces, airports, and other high-security areas, and face recognition technology can help identify potential threats improving public security.
4.2. Law Enforcement
Another domain where face recognition can be proved very useful in law enforcement. It can be used to match images of suspects to a saved database with images of known offenders. In this way, police can solve crimes with faster and more accurate identifications.
4.3. Human-Computer Interaction
Face recognition technology can provide a natural and intuitive interaction between a user and a computer. For example, facial expressions and gaze tracking can be input modalities for augmented or virtual reality systems.
4.4. Healthcare
The impact of face recognition in healthcare can be huge. Plenty of medical applications can be implemented to provide patient identification and ease the function of a hospital. Also, these models can be used to detect specific facial markers for diagnosing medical conditions at an early stage.
5. Challenges
Despite the significant advancements in facial recognition models, they present some important challenges we should consider.
5.1. Variability Appearance
The most significant challenge is that facial images can exhibit large variations due to changes in head pose, facial expressions and occlusions. For example, recognizing the identity of a person that wears a mask is difficult since the mouth, and the nose are not apparent.
5.2. Privacy and Ethical Concerns
Another concern of face recognition is related to privacy. Surveillance systems have access to very sensitive visual information since they record the activity of every citizen. Therefore, there is a high need for strict regulations to protect individuals’ privacy rights.
6. Conclusion
In this article, we discussed face recognition and its importance in today’s machine-learning era.
First, we described how deep learning deals with face recognition and then discussed some of its applications and challenges.