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Artificial intelligence has revolutionized many aspects of life, with facial recognition emerging as one of its most prominent applications. Through advanced biometric authentication methods, this genre of AI can determine and authenticate a person's identity by analyzing their facial features from videos or images.
The Importance of Facial Recognition
With each passing year, the investments in facial recognition companies have been surging, reaching a zenith in 2021 with a spike in venture capital funding. The increasing advancements of this technology have given rise to new applications and business avenues across diverse sectors. These include healthcare, security, transport, marketing, and even exam invigilation.
Understanding Facial Recognition
Facial Recognition leverages artificial intelligence (AI) and machine-learning (ML) to distinguish faces from an image or a video's background. The process initiates by identifying general facial attributes like eyes, mouth, and nose, and further specialized features such as iris, nostrils, and eyebrows. Once the features have been captured, further verification steps are taken using extensive datasets incorporated with both negative and positive images to confirm the facial identity.
Facial recognition methodologies include feature-based, appearance-based, knowledge-based, and template matching methods, all with their own set of strengths and weaknesses. Feature-based methods depend on specific features such as eyes and nose while considering the influence of light and noise variations. Appearance-based methods use statistical analysis and ML to compare facial attributes. Knowledge-based methods rely on predetermined rules, while template matching methods compare images with pre-recorded face traits.
Facial Recognition Systems
Through computer evaluation, facial recognition systems identify key facial markers and compare them against existing databases for potential matches. These technologies employ sophisticated algorithms in machine vision, modeling and reconstruction, and analytics, tapping into the fast-evolving fields of ML, DL, and CNN.
With the advent of facial recognition, various face mapping and data storage systems have surfaced, boasting varying levels of accuracy and efficiency. These techniques generally fall into three categories: Traditional facial recognition, biometric facial recognition, and 3D facial recognition.
Traditional Facial Recognition
This technique takes two forms; Holistic Facial Recognition, focusing on the entire face to identify the desired features, and feature-based facial recognition that extracts significant data for comparison with potential matches. This multi-step method involves detecting the facial identifier in an image, using algorithms to analyze facial characteristics, and comparing facial fingerprints with a database for identification.
Biometric Facial Recognition
Considering skin and facial biometrics as a growing trend within facial scanners, this method could potentially enhance the accuracy of facial recognition technology significantly. Skin texture analysis surveys specific skin regions of the subject, measuring details like wrinkles, pores, and skin textures using a dedicated algorithm.
3D Facial Recognition
This technique creates a 3-dimensional facial representation using the face’s unique geometry. It examines features like eye sockets, noses, chins, focusing on regions with prominent bone structure that doesn't change over time. Offering greater precision compared to its 2D counterpart, 3D facial recognition can even perform scans in the dark and from multiple angles, a feat traditional facial recognition systems cannot achieve.
