Biometric identification: Face Recognition and Face Comparison

Biometrics is increasingly used for the identification and authentication of a person through Face Recognition and Face Comparison technologies.

Facial biometrics acquires a face from an image or video and transforms it into digital data based on the facial features: eye spacing, distance from the nose, lip contour, etc.

Before getting to the heart of the topic, it is good to understand the difference between terms that are often confused with each other.

Difference between Face Detection, Face Recognition and Face Comparison

Face Detection is the technology capable of locating the presence of people’s faces within video streams or digital images. To do this, it uses machine learning and algorithms built to recognize human faces within images with multiple objects. Face detection is the basis of the Face Recognition and Face Comparison processes. The uses are various, such as:

• Focus in cameras
• Marketing: an advertisement is made visible only when the face is recognized
• Counting of people in a certain area (let’s think about how useful it would be in times of Covid-19 to avoid crowds)

Face Recognition, on the other hand, attempts to establish a person’s identity starting from the facial features. In fact, it goes beyond the recognition of the presence of the face. The recognition process begins with the acquisition of a digital image of the face. Then the Face Recognition software compares that data with the images of a database of archived records.

Face Comparison, unlike the Face Recognition which answers the question “who is it?”, Answers the question “why do the two faces correspond?”. It works very similar to facial recognition. It starts with the acquisition of an image that is compared with other images (present in the database or entered as input for comparison). The discrepancy lies in the result. In fact, the Face Comparison returns numerical values ​​that explain why the faces of two different images correspond to the same person. To do this, it analyze the features of the face and their proportions.

The Face Recognition and Face Comparison market

A 2019 study estimated that by 2024 the global Face Recognition market, therefore also of the Face Comparison which is an evolution of it, will generate 7 billion dollars. The main areas of development will be Public Security and Health.

Public Security

The benefits of Face Identification and Face Comparison in crime detection and prevention are innumerable. Let’s see some examples of the use of this technology:

• Prevention of fraud and identity theft
• Border checks by comparing the passport photo with the person’s real face
• Police checks
• Find missing children
• Identify and track down criminals

Health

Biometric identification systems are already in action in this area and great progress has been made in recent years. In fact, it is already possible to detect genetic diseases such as DiGeorge syndrome with a success rate of 96.6%.

Pragma Etimos: Face Recognition and Face Comparison Solutions

Pragma Etimos develops Face Recognition and Face Comparison solutions tailored to customer needs. Starting from an image, we are able to:

– Identify the person

– Establish age and gender

– Compare different images and explain why the two faces correspond to the same person

Furthermore, we develop software aimed at analyzing visual data flows (videos and images) which, through machine learning technologies and algorithms, are able to identify vehicles, license plates, objects of various kinds, colors, people, animals and extract various information from it.

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