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Machine learning algorithms can be used to improve the authentication process based on the results supplied by AI systems.
FREMONT, CA: Artificial intelligence-based authentication systems and machine learning-based predictive analytics are already making our lives easier. Soon, these solutions will extend beyond basic sign-in methods to include other areas where user data security is critical. Machine learning algorithms can be used to improve the authentication process based on the results supplied by AI systems.
Biometric verification is one of the most common and user-friendly ways of authentication. This can be done in a variety of ways, including:
Fingerprint recognition: A black and white image of the finger is sent to the fingerprint scanner. Deep learning algorithms are then used to extract the image's complex elements.
Iris recognition: Every person on the planet has a unique iris pattern, which is processed using image processing techniques and classified using a decision tree algorithm.
Palm recognition: This is a technology that is primarily employed in hospitals. The palm is scanned with infrared sensors, and the resultant image is a duplicate of the blood vessels. After that, machine learning methods are used to classify the data.
Voice Recognition: When one delivers a vocal input, the analog data is captured and translated into digital data using neural networks.
Facial recognition: A face image is recorded, then deep learning techniques are used to evaluate and process features such as the alignment, size, and shape of the face.
Verifying behavioral dynamics is another authentication technique that is used with machine learning. Every unique human being has an innate tendency that machine learning algorithms can examine. The following are some examples of behaviors to consider:
Keystroke Dynamics: It is a study of how quickly a user types a password on their keyboard.
Mouse Dynamics: Mouse dynamics are concerned with how the user interacts with the mouse or touchpad.
Hardware Interaction: This refers to how and where the user interacts with their gadgets.
These data can be recorded, and once the system has become familiar with the user, changes in behavioral patterns can be used to detect identity change.