With rising security concerns because of crime, terrorism, and civil agitations, we need quality surveillance applications more than ever before. While we think about how we can improve public safety, technology does its work. Surveillance tools have become effective thanks to face recognition implementation. It enables real-time identification, access control, and crowd monitoring. Let’s see how face recognition works in surveillance applications and how it enforces our security.
How Does Face Recognition Work?
Face recognition is part of computer vision. It consists of elaborated techniques and deep learning algorithms that make it work. In comparison to the broader notion of computer vision, which allows extracting information from videos and images, face recognition is related to identifying and verifying faces. To be precise, face recognition requires the following steps to work effectively:
- Data collection. AI neural networks are trained on raw data, which is first collected from various sources. Millions of images contain faces with various parameters. They contain different ages, genders, and ethnicities with various facial expressions.
- Data processing. Before the utilization of data for deep learning training, it is classified and standardized. This also includes synchronizing data in the same format and reducing the noise of the initial images.
- Data annotation. For accurate face recognition algorithms, we never miss the step with computer vision services. It includes data annotation that improves the performance of computer vision models. Data annotation involves semantic segmentation and image categorization. It also works with video annotation, or annotation with key points.
- Face detection. After all the key facial features have been identified, a machine can identify and recognize a face based on initial parameters. This part of the training is the most important one, since a machine learns to identify faces from other objects and backgrounds.
- Testing. After the accomplishment of the training procedure, the testing comes into play, which allows us to assess an AI model’s performance. On this stage, it is possible to rectify and retrain the model for better performance and accuracy.
- Deployment. After the training, the face recognition is integrated across a range of applications and systems. The final product is deployed in surveillance applications and security tools.
Face Recognition And Surveillance
Face recognition allows identifying a person from the first seconds they appear before the camera. This becomes possible with new advancing technologies. Through precise training of AI tools, it accomplishes the following functions in surveillance:
- Identification and verification. This is the most common function. It allows detecting people based on the database of images used for the computer vision training. It usually happens in real-time.
- Monitoring and tracking. Face recognition also allows monitoring individuals’ motions through different cameras and locations.
- Anomaly detection. The initial training shows patterns of normal behavior or normal movements. After the training, the algorithms can monitor and detect any deviation (such as sudden motions, running, etc).
- Security decision-making. The real-time visual analysis helps machines to make security decisions. They thus activate alarms and preventive security measures.
5 Real-Life Applications Of Face Recognition In Security
The integration of face recognition technology in security has already conquered public places. From mass events to airport baggage and parking checking, this field of computer vision influences our feeling of control.
Here are the most widespread examples of its usage:
- Public safety. The technology is often used by police and legal surveillance systems.
- Financial institutions. Face identification is a reliable authentication method for all payment transactions.
- Border control. Computer vision penetrates passport tracking, identity checks, and criminal identification. The technology has become popular across airports and border checks.
- Public transport. Cameras in public places help to capture unusual behavior. By tracking crowds, they signal if any preventive intervention is needed.
- Access control. Surveillance systems exist today in all educational institutions. Small and big companies, and even private territories, set them to grant access to authorized users only.
The popularity of this technology across businesses and industries seems to confirm its success. But, does it really help us stay secure and are there any risks associated with its implementation?
Benefits And Risks Of Face Recognition Implementation
Computer vision technology improves surveillance. It identifies dangerous activity and concentrates on public safety. Its face recognition algorithms are useful for ensuring safety. This technology enables fast and contactless identification of individuals. As a result, we improve our capacities of finding suspects and missing people. By monitoring crowds in airports and stations, face recognition serves as an arbiter for public safety. It also helps prevent financial fraud and reduces the need for manual checks in access control. However, great technology comes with risks, mainly concerning data privacy and ethics. The use of biometric data, so widespread in face recognition, raises privacy concerns. The most vulnerable audience is the one under monitoring. That’s why the question of individual and public consent rises. It should be received in every situation before the usage of monitoring. We can reduce the risks, though, by making the technology’s implementation transparent. With personal data protection in mind, we can follow legal regulations on this matter. More and more of them appear on the company’s level as well as on the national level of every country.
Let’s Summarize
Face recognition is one of the fields of AI that brought surveillance and security to the next level. It gave opportunities to be more precise in identifying and verifying individuals. Face recognition makes a significant contribution to public security through improved algorithms. It involves training of neural networks, and making predictions that are more accurate. Now that we can recognize possible dangers and unusual behavior, we can make decisions in real time. As a result, we proactively remove any hazards to our safety. Face recognition requires complex training. It involves large-scale datasets and organized and labelled data. But with deep learning technology and continuous training, it opens us doors to powerful safety tools. We must always keep in mind a strategy that balances the need to protect people’s privacy with ethical and safety concerns. If managed wisely, this technology will support our collective security.
