Facial Recognition for Computer Vision

How Data Collection Plays a Crucial Role in Developing Facial Recognition Models

Humans are adept at recognizing faces, but we also interpret expressions and emotions quite naturally. Research says we can identify personally familiar faces within 380ms after presentation and 460ms for unfamiliar faces. However, this intrinsically human quality now has a competitor in artificial intelligence and Computer Vision. These pioneering technologies are helping develop solutions that recognize human faces more accurately and efficiently than ever.

These latest innovative and non-intrusive technologies have made life simpler and exciting. Face recognition technology has grown into a fast-developing technology. In 2020, the facial recognition market was valued at $3.8 billion, and the same is slated to double in size by 2025 – forecasted to be over $8.5 billion.

What is Facial Recognition?

Facial recognition technology maps facial features and helps identify a person based on the stored faceprint data. This biometric technology uses deep learning algorithms to compare the stored face print with the live image. Face detection software also compares captured images with a database of images to find a match.

Facial recognition has been used in many applications for enhancing security in airports, helps law enforcement agencies in detecting criminals, forensic analysis, and other surveillance systems.

How does facial recognition work?

Facial recognition software begins with facial recognition data collection and image processing using Computer Vision. The images undergo a high level of digital screening so that the computer can differentiate between a human face, a picture, a statue, or even a poster. By using machine learning, patterns and similarities in the dataset are identified. The ML algorithm identifies the face in any given image by recognizing facial feature patterns:

  • The height to the width ratio of the face
  • The color of the face
  • The width of each feature – eyes, nose, mouth, and more.
  • Distinctive features

As different faces have different features, so does facial recognition software. However, in general, any facial recognition works using the following procedure:

  1. Facial detection

    Facial technology systems recognize and identify a facial image in a crowd or individually. Technological advancements have made it easier for the software to detect facial images even when there is a slight variation in posture – facing the camera or looking away from it.

  2. Facial analysis

    Facial analysis for facial recognition Next is the analysis of the captured image. A face recognition system is used to accurately identify unique facial features such as the distance between eyes, length of the nose, space between mouth and nose, width of the forehead, the shape of the eyebrows, and other biometrical attributes.

    A human face’s distinct and recognizable features are called nodal points, and every human face has about 80 nodal points. By mapping the face, recognizing geometry, and photometry, it is possible to analyze and identify faces using the recognition databases accurately.

  3. Image Conversion

    After capturing the image of a face, the analog information is converted into digital data based on the person’s biometrics features. Since machine learning algorithms only recognize numbers, converting the facial map into a mathematical formula becomes pertinent. This numerical representation of the face, also known as a faceprint, is then compared with a database of faces.

  4. Finding a match

    The final step is comparing your face print with several databases of known faces. The technology tries to match your features with those in the database.

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The matched image is usually returned with the name and address of the person. If such information is missing, the data saved in the database is used. 

Facial Recognition Technology Industry Applications

Facial recognition industry applications

  • We all know of Apple’s Face ID that helps its users quickly lock and unlock their phones and log into applications.
  • McDonald’s has been using facial recognition in its Japanese store to assess the quality of customer service. It uses this technology to determine whether its servers are assisting its customers with a smile.
  • Covergirl uses facial recognition software to help its customers select the right shade of foundation. 
  • MAC is also using sophisticated facial recognition to provide brick and mortar style shopping experience to customers by allowing them to virtually ‘try’ their makeup using augmented mirrors. 
  • Fast food giant, CaliBurger, has been using facial recognition software to allow its patrons to view their previous purchases, enjoy specialized discounts, view personalized recommendations, and use their loyalty programs. 
  • US-healthcare giant Cigna lets their customers in China file their health insurance claims using photo signatures instead of written signs. 

Data Collection for Facial Recognition Model

For the facial recognition model to perform to its maximum efficiency, you must train it on various heterogeneous datasets.

Since facial biometrics differs from person to person, the facial recognition software should be adept at reading, identifying, and recognizing every face. Moreover, when the person shows emotions, their facial contours change. The recognition software should be designed so that it can accommodate these changes.

One solution is receiving photos of several people from various parts of the world and creating a heterogeneous database of known faces. You should ideally take photos from multiple angles, perspectives and with a variety of facial expressions. 

When these photos are uploaded to a centralized platform, clearly mentioning the expression and perspective, it creates an effective database. The quality control team can then sift through these photos for quick quality checks. This method of collecting pictures of different people can result in a database of high-quality, highly-efficient images.

Wouldn’t you agree that facial recognition software will not work optimally without a reliable facial data collection system?

Facial data collection is the foundation for any facial recognition software’s performance. It provides valuable information such as the length of the nose, the width of the forehead, the shape of the mouth, ears, face, and much more. Using AI training data, automated facial recognition systems can accurately identify a face amidst a large crowd in a dynamically changing environment based on their facial features.

If you have a project that demands a highly reliable dataset that can help you develop sophisticated facial recognition software, Shaip is the right choice. We have an extensive collection of facial datasets optimized for training specialized solutions for various projects. 

To know more about our collection methods, quality control systems, and customization techniques, get in touch with us today.

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