Help: Glossary

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This page will give a short overview over the most important terms used in the Wiki.

A[ ]

Access Control[ ]

  • Systems used to authenticate individuals who can access an area (whitelist) or who are banned from it (blacklist).

Anomaly Detection[ ]

  • Identification of unusual behavior or events in data that deviate from expected patterns.

Audio Recognition[ ]

  • The ability of a software to identify/understand certain sounds. From a technological perspective, software processes audio relatively similarly to how video is processed: rather than feeding an image, a spectrogram is used as input for the software.

Automated Payments[ ]

  • Systems using computer vision to connect to payment services.

B[ ]

Behavioural Data[ ]

  • Behavioural data is the data collected related to the way in which individuals uniquely behave (facial expressions, body movements, voice, etc.).

Biometric Data[ ]

  • Biometric data is all data related to the body, which can be used to identify or monitor individuals or groups of individuals and is impossible or very difficult to alter (face, fingerprints, iris, etc.).

Border Control[ ]

  • Systems used to identify travelers at the border.

Biometric Identification[ ]

Biometric Mass Surveillance[ ]

  • Biometric Mass Surveillance is a form of monitoring, tracking, or processing of personal (biometric and behavioural) data of individuals indiscriminately and in a generalised manner without a prior criminal suspicion (FRA, 2019).
  • Additionally, this surveillance occurs at a distance, in a public space and in a continuous or ongoing manner by checking them against data stored in a database.

C[ ]

Control and Command Center[ ]

  • Centralized facility for monitoring and managing security operations, often equipped with technology for real-time decision-making.

Controlled Images[ ]

  • Controlled images are images that are captured for the purpose of processing, aimed at optimal positions and lighting conditions. They are for example taken at a police station, or at a photographer’s studio with strict requirements, and are either contained in databases that precede the introduction of a facial recognition system (e.g., driver’s license databases) or are specifically designed to match high criteria of biometric systems (i.e., photographs for biometric passports).

Cooperative Searches[ ]

Crowd Management[ ]

  • Automated analysis of sensor data in order to regulate the movement and behavior of large gatherings.

D[ ]

Database Software[ ]

  • A software used for the management of databases
  • Allows for the mannipulation (Creation, Deletion, Changing) of data within a database.

Datasets[ ]

  • A dataset is a structured collection and storage of data. For the Wiki, it often involves the structured collection and storage of biometric data, such as fingerprints, facial images etc.

Deployments[ ]

  • For the Wiki, deployments are all instances in which one or more specific surveillance products (for example a smart cameras system) are deployed in a specific location, by specific institutions. Deployments can have a start and end date, can be open or closed, depending on how much is known about them.

Detection[ ]

E[ ]

Ear Accoustics[ ]

  • The unique characteristics of the ear canal are used to identify an indivudal.
  • To determine the identity the unique echo sound characteristics of each human are taken into account.
  • This technology allows for the authentication of an identity by simply wearing headphone with a microphone.

Emotion recognition[ ]

  • Software that categorises facial expressions into emotion categories – happiness, sadness, anger, etc. – is known to be used in billboards that are equipped with cameras, in order to analyse audience response to advertisements. For example, in airports or at train stations. While the face is claimed to be a “window into the brain” by some, the technology has been heavily criticised. Firstly, some consider it an undesirable invasion of their privacy, while other critique the technology for capturing primarily stereotypical ways of expressing oneself (van de Ven, 2017). In some places, such as at Dutch train stations, these critiques have led to disabling the cameras in billboards altogether (Het Parool, 2017).

F[ ]

Face Mask Recognition[ ]

  • Identification of individuals wearing masks, used for enforcing safety protocols or security measures during public health crises.

Facial Recognition (Identification)[ ]

  • One-to-many (1:N) searches are called identification searches. An unknown single face, picked up for example from surveillance video footage or from a passport, is run against a large dataset of known faces, in order to identify the unknown face, or to determine if it occurs on a so called “watchlist”. This can be done in the case of forensic investigations or can be deployed in remote biometric identification scenarios in the public space.

Facial Recognition (Verification/Authentication)[ ]

  • One-to-one (1:1) searches are called verification or authentication searches and are used to determine whether an individual face presented to the camera matches a single face stored in the system. This is how “Face ID” works on iPhones for example. In this example, people volunteer the capture of their face, they are thus considered in a “cooperative” scenario.

Facial Recognition - Age Recognition[ ]

  • Identification of a person's age range based on facial characteristics, used for demographic analysis or age-restricted access control.

Facial Recognition - Gender Recognition[ ]

  • Determination of a person's gender based on facial characteristics, used for demographic analysis or targeted advertising for example.

Facial Recognition - Live[ ]

  • Real-time facial recognition technology for instant identification or monitoring of individuals.

Facial Recognition - Retrospective (Forensic)[ ]

  • Analysis of recorded video footage to retrospectively identify individuals using facial recognition algorithms.
  • Forensic facial recognition is carried out generally in the context of judicial investigations in order to match photographs of persons of interest captured via surveillance cameras or extracted from documents to an operational database of known individuals (Al-Kawaz et al.2018). This is contrary to live facial recognition.

Finger Vein Recognition[ ]

  • Using the pattern of blood vesses visible on the finger allows for the authentication of an individual.
  • Matching the unique pattern to a previously known image.
  • Meassuring blood flow makes identifying fraudsters more easily.

Fingerprint Recognition[ ]

  • The unque ridges on the fingers are used to authenticate/identify a person.
  • The unique ridges are matched against a previously recorded impression of the finger ridges.

Forensic Facial Recognition[ ]

  • See Facial Recognition - Retrospective (Forensic)

G[ ]

Gait Recognition[ ]

  • Gait recognition consists of recognising the specific way in which a person walks (gait), but in reality it covers a broader range of criteria (body, proportions, posture, etc.) (Segal 2020,2).
  • The advantages of gait recognition are that it does not require a clear access to a face, and it requires a lower image resolution (as it analyses an entire body, not only a face)
  • Gait recognition, however, requires more computing power because it works on the basis of moving images (i.e., multiple frames of still images, up to 30 frames per second) rather than still images.

Gunshot Detection[ ]

  • Automated systems for identifying and locating gunfire using acoustic sensors or analysis of audio signatures.

H[ ]

I[ ]

Identification[ ]

Institutions[ ]

  • For the Wiki, institutions are all entities that deploy biometric surveillance technology. For example, companies, labour unions, NGOs, governmental organisations, law enforcement, universities, etc.

Iris Recognition[ ]

  • The patterns of one or both irises of an individual are used for authentification or identification by using mathematical pattern recognition techniques.
  • The patterns are unique to each individual.
  • This technology performs especially well in one-to-many searches by avoiding false matches.

J[ ]

K[ ]

L[ ]

Live Facial Recognition[ ]

  • Live facial recognition uses live video feeds in order to generate snapshots of individuals and then match them against a database of known individuals – the “watchlist”. It is the most controversial deployment of facial recognition (Fussey and Murray 2019).

M[ ]

Machine Learning[ ]

The use and development of software that is able to adapt and change its behaviour automatically based on algorithms and statistical models.

Movement Recognition[ ]

  • Detection and analysis of human movement patterns, used for for categorizing behavior or tracking individuals.

N[ ]

Neural Networks[ ]

Non-Cooperative Searches[ ]

  • Non-cooperative searches are searches without the intention or consent of the individuals.
  • This is the oposite of cooperative searches.

O[ ]

Object Detection[ ]

  • Identification and location of objects within images or video streams.

Object Tracking[ ]

  • Monitoring the movement of objects over time within a scene.

P[ ]

Palm Print Recognition[ ]

Biometric identification technology that analyzes the unique patterns and characteristics present in an individual's palm to verify or authenticate their identity.

Pattern Recognition[ ]

  • Identification and interpretation of patterns within data, used for example in anomaly detection or image analysis.

People Tracking and Counting[ ]

  • An object detection algorithm estimates the presence and position of individuals on a camera image. These positions are stored or counted and used for further metrics.
  • It is used for example to count passers-by in city centres, and for a one-and-a-half-meter social distancing monitors.
  • See also Person Detection

Person detection[ ]

  • Person detection denotes the ability of a software application to estimate (as in, provide a statistical probability) whether an object in the camera image is a person.
  • Generally, it is able to indicate the position of the person in the image.
  • Person detection systems can be used in basic analytics scenarios, where for example the presence of people is counted. Moreover, object detection algorithms can be used to track individuals between video frames, although they generally have a hard time tracking occlusions (people walking in front of others, hiding them from the camera) and specific people across multiple camera viewpoints. Person detection does not obtain any information about individuals faces.

Predictive Policing[ ]

  • Use of data analysis and predictive algorithms to anticipate and prevent criminal activity, aiming to determine resource allocation and crime prevention strategies.

Products[ ]

  • For the Wiki, products are different software solutions or technologies that enable the analysis and input of biometric data.

Public Spaces[ ]

  • Public spaces are spaces in which a general population has access to.
  • Public spaces can be publicly owned (roads, streets, city squares, parking facilities, government facilities) or privately owned (shopping malls, stadiums).

Q[ ]

R[ ]

Recognition[ ]

See Facial Recognition (Identification), Facial Recognition (Verification/Authentication) and Live Facial Recognition.

S[ ]

Smartphone App[ ]

  • Mobile application software designed for smartphones.

Software[ ]

Software is a set of instructions, data or programs used to operate computers and execute specific tasks.

Semi-Supervised Machine Learning[ ]

Supervised Machine Learning[ ]

  • Supervised machine learning consists of teaching the system to recognise people, cars,guns, or any other object by feeding it an annotated dataset of such objects.
  • It is supervised because humans “supervise” how the computer learns, by annotating the dataset (“this is a car”, “this is a gun” etc.). The categories of the annotations (cars, guns, etc.) will thus be the only ones that the system will be able to recognise.
  • Most video surveillance systems use supervised machine learning (IPVM Team 2021a, 11)
  • See also Semi-Supervised Machine Learning and Unsupervised Machine Learning.

Social Distancing Monitor[ ]

  • Device or system for enforcing social distancing measures in public spaces, often utilizing sensors or cameras to detect proximity between individuals.

Social Media Monitoring[ ]

  • Surveillance of social media platforms to gather intelligence, monitor public sentiment, or detect potential security threats.

T[ ]

Thermal Camera[ ]

  • Imaging device capable of detecting and visualizing heat signatures, useful for surveillance, perimeter security, and search and rescue operations.

U[ ]

Uncontrolled Images[ ]

  • Uncontrolled images are images that are captured outside of specific requirement, collected for example through social media scraping or video surveillance.
  • This is the oposite of controlled images.

Unsupervised Machine Learning[ ]

  • Unsupervised machine learning lets the system cluster objects by itself without the input of an human.
  • The advantage is the open-endedness of the systems (meaning they can generate categories of objects not anticipated in the training dataset), but the disadvantage is that algorithms can potentially cluster objects along irrelevant criteria for the task (for example clustering red motorcycles, cars, and trucks in one group and green ones in another, as opposed to creating one cluster for all motorcycles, one for cars and one for trucks)
  • Also see Supervised Machine Learning and Semi-Supervised Machine Learning

V[ ]

Video Analysis[ ]

  • Automated analysis of video footage to extract relevant information, detect anomalies, or identify objects or individuals of interest.

Video Summarization[ ]

  • Condensation of lengthy video footage into shorter summaries or key highlights, facilitating review and analysis by security personnel.

Violence Detection[ ]

  • Systems for automatically identifying violent behavior or incidents within video streams, aiming to enhance public safety and emergency response.

Voice Recognition[ ]

  • Technology for identifying and interpreting spoken words or phrases, commonly used for authentication or voice-controlled systems.

W[ ]

X[ ]

Y[ ]

Z[ ]