Face Recognition - Armatura

Armatura's Facial Recognition Technology Unlocking The Future With Precision And Protection

The advancement of facial recognition technology to a level comparable to, or surpassing, human recognition capabilities has become a reality. The ability of humans to recognize others is based on encoding specific facial features in our memory. However, human memory is limited, making it difficult to recall vast amounts of information accurately. In the information age, it is crucial to train machines to perform recognition tasks on massive amounts of data, with facial recognition being the most effective solution, as it closely mimics the biological wayhumans recognize others.

Facial recognition Can be categorized into 1:1 and 1:N modes

1:1 identifies "you are you "

1:1 is usually used for “identity verification”, combining Individual’s ID document with facial recognition technology. The system checks the identity document, then captures the person’s face in the image and extracts the unique features of the face, such as facial contours, eyes and mouth etc. The system then converts the detected face into digital signal and extracts the unique features of the face to form a biometric template. The template is then compared with the facial template converted from the face image stored in the ID document to determine whether the ID document holder is the actual person represented by the ID document.

1:N identifies "who you are"

1:N means “Identification mode”, which relies on an established facial information database. The system collects facial image and extracts the unique features of the face, such as facial contours, eyes and mouth etc. The system then converts the detected face into a digital signal, and extracts the unique features of the face to form a biometric template. The system then compares it with the facial information in the database to determine the identity of the person being recognized.

Development and future situation

Due to the rapid speed of facial recognition technology, detecting its fake presence from a real live person can be challenging, leading to concerns about security and privacy. This has been a significant consideration throughout the entire development cycle of facial recognition, with developers ensuring that the process and scope of data collection are authorised by users. From a technical or procedural standpoint, the entire process should be safe and controllable. In recent years, the advance of machine learning-based artificial intelligence, speed, seamlessness, and security compared to traditional methods such as card swiping, passwords, and fingerprints, facial recognition technology has been widely adopted and favoured by users in various industries for access control and management. Due to the rapid speed of facial recognition technology, detecting its fake presence from a real live person can be challenging, leading to concerns about security and privacy. This has been a significant consideration throughout the entire development cycle of facial recognition, with developers ensuring that the process and scope of data collection are authorised by users. From a technical or procedural standpoint, the entire process should be safe and controllable. In recent years, the advance of machine learning-based artificial intelligence, speed, seamlessness, and security compared to traditional methods such as card swiping, passwords, and fingerprints, facial recognition technology has been widely adopted and favoured by users in various industries for access control and management.

Facial Recognition Process

Accuracy towards Facial Variation

Armatura’s facial recognition technology uses deep learning algorithms that are capable of learning and adapting to changes in facial features over time. This allows for more accurate and reliable recognition, even in scenarios where facial features may have changed due to aging, facial hair, or other factors.Another advantage of Armatura’s facial recognition technology is its ability to handle variations in facial expression and pose. The technology uses advanced algorithms that can recognize faces even when they are tilted, turned, or partially obscured. This allows for more flexible and versatile deployment of the technology in various scenarios.