"A facial recognition system is a technology potentially capable of matching a human face from a digital image or a video frame against a database of faces."
The process of identifying human faces within images or video frames.
Image representation and manipulation: Understanding how digital images are stored, processed and modified is vital to building face recognition algorithms.
Feature extraction: This involves extracting meaningful information from the image, such as the shapes and contours of the face, and coding them into a numerical vector.
Machine learning: This is a fundamental component in building face recognition systems, as it provides the algorithm with the ability to learn the features associated with a specific face.
Pattern recognition: This is the process of identifying patterns or features in an image that are associated with a specific face or group of faces.
Convolutional neural networks: These are a type of neural network that are commonly used in face recognition, as they can efficiently extract features from large datasets.
Deep learning: This is an advanced form of machine learning that is capable of detecting complex patterns and features in large datasets.
Principal component analysis: This is a statistical technique used to reduce the dimensionality of large datasets, allowing algorithms to more efficiently process and analyze the images.
Support vector machines: These are another type of machine learning algorithm used in face recognition, which are particularly useful in accurately classifying images.
Facial landmark detection: This involves identifying key points on the face, such as the eyes, nose and mouth, and using these landmarks to locate and recognize specific features.
3D face reconstruction: This technology involves the creation of a 3D model of the face, which is more effective in recognizing facial features than 2D images.
2D Face Recognition: This type of face recognition uses images of faces taken by a 2D camera to identify individuals.
3D Face Recognition: This type of face recognition is similar to 2D face recognition, but uses a 3D camera that captures depth and texture information to identify individuals.
Thermal Imaging: This type of face recognition uses thermal imaging cameras to capture the heat signatures of an individual's face and then matches it against a database of known images.
Laser-based Face Recognition: This type of face recognition uses lasers to capture an individual's facial features, such as the distance between eyes and nose, and uses this information to identify them.
Skin Texture Analysis: This type of face recognition uses the unique patterns on an individual's skin to identify them. This method is particularly useful in low-illumination environments where other methods may not work.
Infrared Imaging: This type of face recognition uses infrared cameras to capture an individual's face and detect the heat signatures that are unique to each person.
Geometric Face Recognition: This type of face recognition uses the geometry of the face, for example, the distance between the eyes, the shape of the nose, and the width of the mouth, to identify individuals.
Hybrid Face Recognition: This type of face recognition uses a combination of two or more face recognition techniques to improve accuracy and overcome the limitations of individual methods.
Motion-based Face Recognition: This type of face recognition uses video footage with an individual's face in motion to identify them. It can be particularly useful in surveillance applications.
Feature-based Face Recognition: This type of face recognition uses specific facial features, such as the eyes, nose, or mouth, to identify individuals. This technique is particularly useful when trying to identify individuals from low-quality images.
Neural Network Face Recognition: This type of face recognition uses deep learning algorithms and neural networks to recognize patterns and identify individuals.
Expression-based Face Recognition: This type of face recognition uses an individual's facial expressions to identify them. It can be particularly useful when working with video footage of a person's face.
Age Progression Face Recognition: This type of face recognition uses machine learning algorithms to predict how a person's face will look as they age.
Sketch-based Face Recognition: This type of face recognition uses sketches or artistic representations of a person's face to identify them.
Masked Face Recognition: This type of face recognition uses AI to recognize individuals even when they are wearing masks or face coverings.
Partial Face Recognition: This type of face recognition can recognize an individual even when only a portion of their face is visible, such as in a side profile or partial facial image.
Gait Recognition: This type of face recognition uses the way an individual walks to identify them, by analyzing the unique characteristics of their gait.
Multispectral Face Recognition: This type of face recognition uses multiple different types of sensors or cameras to capture and analyze facial data, combining data from different sources to improve accuracy.
"Such a system is typically employed to authenticate users through ID verification services, and works by pinpointing and measuring facial features from a given image."
"Development began on similar systems in the 1960s, beginning as a form of computer application."
"Facial recognition systems have seen wider uses in recent times on smartphones and in other forms of technology, such as robotics."
"Because computerized facial recognition involves the measurement of a human's physiological characteristics, facial recognition systems are categorized as biometrics."
"The accuracy of facial recognition systems as a biometric technology is lower than iris recognition, fingerprint image acquisition, palm recognition or voice recognition."
"It is widely adopted due to its contactless process."
"Facial recognition systems have been deployed in advanced human–computer interaction, video surveillance, law enforcement, passenger screening, decisions on employment and housing, and automatic indexing of images."
"Facial recognition systems are employed throughout the world today by governments and private companies."
"Some systems have previously been scrapped because of their ineffectiveness."
"The use of facial recognition systems has also raised controversy, with claims that the systems violate citizens' privacy, commonly make incorrect identifications, encourage gender norms and racial profiling, and do not protect important biometric data."
"The appearance of synthetic media such as deepfakes has also raised concerns about its security."
"These claims have led to the ban of facial recognition systems in several cities in the United States."
"Growing societal concerns led social networking company Meta Platforms to shut down its Facebook facial recognition system in 2021."
"Deleting the face scan data of more than one billion users."
"The change represented one of the largest shifts in facial recognition usage in the technology's history." (Note: Due to the limitations of the text-based interface, the quotes provided might be slightly modified for coherence while retaining the essence of the original information.)