How Does Facial Scanning Technology Work?

Learn how facial scanning technology works and its applications in various industries. Discover the impact of this innovative technology on security and access control.

Facial recognition technology , also known as facial scanning, is one of the most popular biometric technologies today. With its ability to quickly and accurately identify individuals, this system has been applied in a variety of areas, from security, transaction verification, to everyday use. However, many people may still wonder how this technology works. Let’s find out in this article.

Working principle of face scanning system
1. Image Acquisition
The first step of the face scanning system is to acquire an image of the person’s face using a digital camera or video camera, which may be a high-resolution camera, infrared camera or 3D camera, depending on the system requirements.

2. Face Detection
Once the image is captured, the system will detect and separate the face from the background using image processing algorithms such as Haar Cascade or Deep Learning-based detectors.

3. Image Pre-processing
The resulting face image is adjusted to suit the analysis, such as adjusting the brightness, rotating the image to be straight, or resizing it to be normal.

4. Feature Extraction
In this step, the system analyzes and extracts facial features such as the distance between the eyes, the shape of the nose, lips, and bone structure using various techniques such as:

  • Geometric Feature-based Methods: Measure the distance and proportion of important points on the face.
  • Appearance-based Methods: Analyze the overall appearance of the entire face.
  • 3D Model-based Methods: Create 3D models of the face for more accurate analysis.

5. Template Creation
The extracted features are converted into digital data, also known as a “template,” which is a mathematical representation of the face.

6. Comparison and matching (Matching)
When an identity is required, the system compares the created template with the existing database using a matching algorithm, such as:

  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Elastic Bunch Graph Matching (EBGM)
  • Deep Learning-based methods such as Convolutional Neural Networks (CNNs)

7. Decision Making
The system will process the similarity between the analyzed template and the data in the database and decide whether it is the same person or not based on the specified confidence score.

Challenges and developments of facial scanning systems
Although facial scanning technology is highly efficient, there are still many challenges, such as:

  1. Accuracy in various environments, such as low light or improper camera angles
  2. Facial changes over time, such as growth and aging
  3. Facial disguising or the use of masks
  4. Privacy concerns and misuse of data

To address these challenges, researchers and developers are constantly inventing new techniques.

  • Using AI and Deep Learning to Increase Recognition Accuracy
  • Development of Liveness Detection System to Prevent Forgery
  • Use of encryption technology to secure personal data

Facial scanning technology works on the principles of image processing and machine learning to analyze and remember the unique characteristics of each person’s face through various steps, from image acquisition, feature extraction, to comparison and decision-making. Despite the challenges in development, with the advancement of technology, facial scanning systems tend to be more efficient and secure, playing an important role in daily life and future security.