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A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces, typically employed to authenticate users through ID verification services , works by pinpointing and measuring facial features from a given image. While initially 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 categorised as biometrics. Although the accuracy of facial recognition systems as a biometric technology is lower than iris recognition and fingerprint recognition , it is widely adopted due to its contactless process. Automated facial recognition was pioneered in the s. Their early facial recognition project was dubbed "man-machine" because the coordinates of the facial features in a photograph had to be established by a human before they could be used by the computer for recognition.
On a graphics tablet a human had to pinpoint the coordinates of facial features such as the pupil centers, the inside and outside corner of eyes, and the widows peak in the hairline. The coordinates were used to calculate 20 distances, including the width of the mouth and of the eyes.
A human could process about 40 pictures an hour in this manner and so build a database of the computed distances. A computer would then automatically compare the distances for each photograph, calculate the difference between the distances and return the closed records as a possible match.
In Takeo Kanade publicly demonstrated a face matching system that located anatomical features such as the chin and calculated the distance ratio between facial features without human intervention. Later tests revealed that the system could not always reliably identify facial features. But interest in the subject grew and in Kanade published the first detailed book on facial recognition technology.
In the Defense Advanced Research Project Agency DARPA and the Army Research Laboratory ARL established the face recognition technology program FERET to develop "automatic face recognition capabilities" that could be employed in a productive real life environment "to assist security, intelligence, and law enforcement personnel in the performance of their duties".
Face recognition systems that had been trialed in research labs were evaluated and the FERET tests found that while the performance of existing automated facial recognition systems varied, a handful of existing methods could viably be used to recognize faces in still images taken in a controlled environment. Viisage Technology was established by a identification card defense contractor in to commercially exploit the rights to the facial recognition algorithm developed by Alex Pentland at MIT.
Driver's licenses in the United States were at that point a commonly accepted from of photo identification.
DMV offices across the United States were undergoing a technological upgrade and were in the process of establishing databases of digital ID photographs. This enabled DMV offices to deploy the facial recognition systems on the market to search photographs for new driving licenses against the existing DMV database. In Minnesota incorporated the facial recognition system FaceIT by Visionics into a mug shot booking system that allowed police, judges and court officers to track criminals across the state.
Until the s facial recognition systems were developed primarily by using photographic portraits of human faces. Research on face recognition to reliably locate a face in an image that contains other objects gained traction in the early s with the principle component analysis PCA.
Eigenfaces are determined based on global and orthogonal features in human faces. A human face is calculated as a weighted combination of a number of Eigenfaces. Because few Eigenfaces were used to encode human faces of a given population, Turk and Pentland's PCA face detection method greatly reduced the amount of data that had to be processed to detect a face.
Pentland in defined Eigenface features, including eigen eyes, eigen mouths and eigen noses, to advance the use of PCA in facial recognition. While Eigenfaces were also used for face reconstruction. In these approaches no global structure of the face is calculated which links the facial features or parts. Purely feature based approaches to facial recognition were overtaken in the late s by the Bochum system, which used Gabor filter to record the face features and computed a grid of the face structure to link the features.
The so-called "Bochum system" of face detection was sold commercially on the market as ZN-Face to operators of airports and other busy locations. The software was "robust enough to make identifications from less-than-perfect face views. It can also often see through such impediments to identification as mustaches, beards, changed hairstyles and glasses—even sunglasses". Real-time face detection in video footage became possible in with the Viola—Jones object detection framework for faces.
Therefore, the Viola-Jones algorithm has not only broadened the practical application of face recognition systems but has also been used to support new features in user interfaces and teleconferencing. While humans can recognize faces without much effort,  facial recognition is a challenging pattern recognition problem in computing. Facial recognition systems attempt to identify a human face, which is three-dimensional and changes in appearance with lighting and facial expression, based on its two-dimensional image.
To accomplish this computational task, facial recognition systems perform four steps. First face detection is used to segment the face from the image background. In the second step the segmented face image is aligned to account for face pose , image size and photographic properties, such as illumination and grayscale. The purpose of the alignment process is to enable the accurate localization of facial features in the third step, the facial feature extraction.
Features such as eyes, nose and mouth are pinpointed and measured in the image to represent the face. The so established feature vector of the face is then, in the fourth step, matched against a database of faces. Some face recognition algorithms identify facial features by extracting landmarks, or features, from an image of the subject's face.
Other algorithms normalize a gallery of face images and then compress the face data, only saving the data in the image that is useful for face recognition. A probe image is then compared with the face data. Recognition algorithms can be divided into two main approaches: geometric, which looks at distinguishing features, or photo-metric, which is a statistical approach that distills an image into values and compares the values with templates to eliminate variances.
Some classify these algorithms into two broad categories: holistic and feature-based models. The former attempts to recognize the face in its entirety while the feature-based subdivide into components such as according to features and analyze each as well as its spatial location with respect to other features. Popular recognition algorithms include principal component analysis using eigenfaces , linear discriminant analysis , elastic bunch graph matching using the Fisherface algorithm, the hidden Markov model , the multilinear subspace learning using tensor representation, and the neuronal motivated dynamic link matching.
To enable human identification at a distance HID low-resolution images of faces are enhanced using face hallucination. In CCTV imagery faces are often very small. But because facial recognition algorithms that identify and plot facial features require high resolution images, resolution enhancement techniques have been developed to enable facial recognition systems to work with imagery that has been captured in environments with a high signal-to-noise ratio.
Face hallucination algorithms that are applied to images prior to those images being submitted to the facial recognition system utilise example-based machine learning with pixel substitution or nearest neighbour distribution indexes that may also incorporate demographic and age related facial characteristics.
Use of face hallucination techniques improves the performance of high resolution facial recognition algorithms and may be used to overcome the inherent limitations of super-resolution algorithms. Face hallucination techniques are also used to pre-treat imagery where faces are disguised.
Here the disguise, such as sunglasses, is removed and the face hallucination algorithm is applied to the image. Such face hallucination algorithms need to be trained on similar face images with and without disguise. To fill in the area uncovered by removing the disguise, face hallucination algorithms need to correctly map the entire state of the face, which may be not possible due to the momentary facial expression captured in the low resolution image.
Three-dimensional face recognition technique uses 3D sensors to capture information about the shape of a face. This information is then used to identify distinctive features on the surface of a face, such as the contour of the eye sockets, nose, and chin. It can also identify a face from a range of viewing angles, including a profile view.
All these cameras will work together so it can track a subject's face in real-time and be able to face detect and recognize. A different form of taking input data for face recognition is by using thermal cameras , by this procedure the cameras will only detect the shape of the head and it will ignore the subject accessories such as glasses, hats, or makeup. Efforts to build databases of thermal face images date back to In , researchers from the U.
Army Research Laboratory ARL developed a technique that would allow them to match facial imagery obtained using a thermal camera with those in databases that were captured using a conventional camera. Founded in , Looksery went on to raise money for its face modification app on Kickstarter. After successful crowdfunding, Looksery launched in October The application allows video chat with others through a special filter for faces that modifies the look of users.
Image augmenting applications already on the market, such as FaceTune and Perfect, were limited to static images, whereas Looksery allowed augmented reality to live videos. In late SnapChat purchased Looksery, which would then become its landmark lenses function.
DeepFace is a deep learning facial recognition system created by a research group at Facebook. It identifies human faces in digital images. It employs a nine-layer neural net with over million connection weights, and was trained on four million images uploaded by Facebook users. The emerging use of facial recognition is in the use of ID verification services.
Many companies and others are working in the market now to provide these services to banks, ICOs, and other e-businesses. Face ID has a facial recognition sensor that consists of two parts: a "Romeo" module that projects more than 30, infrared dots onto the user's face, and a "Juliet" module that reads the pattern.
The facial pattern is not accessible by Apple. The system will not work with eyes closed, in an effort to prevent unauthorized access. This is done by using a "Flood Illuminator", which is a dedicated infrared flash that throws out invisible infrared light onto the user's face to properly read the 30, facial points. The Australian Border Force and New Zealand Customs Service have set up an automated border processing system called SmartGate that uses face recognition, which compares the face of the traveller with the data in the e-passport microchip.
This program first came to Vancouver International Airport in early and was rolled up to all remaining international airports in — Police forces in the United Kingdom have been trialing live facial recognition technology at public events since Ars Technica reported that "this appears to be the first time [AFR] has led to an arrest".
The U. Department of State operates one of the largest face recognition systems in the world with a database of million American adults, with photos typically drawn from driver's license photos. The FBI uses the photos as an investigative tool, not for positive identification. In recent years Maryland has used face recognition by comparing people's faces to their driver's license photos.
The system drew controversy when it was used in Baltimore to arrest unruly protesters after the death of Freddie Gray in police custody. The FBI has also instituted its Next Generation Identification program to include face recognition, as well as more traditional biometrics like fingerprints and iris scans , which can pull from both criminal and civil databases.
Starting in , U. Customs and Border Protection deployed "biometric face scanners" at U. Passengers taking outbound international flights can complete the check-in, security and the boarding process after getting facial images captured and verified by matching their ID photos stored on CBP's database. Images captured for travelers with U. TSA had expressed its intention to adopt a similar program for domestic air travel during the security check process in the future.
The American Civil Liberties Union is one of the organizations against the program, concerning that the program will be used for surveillance purposes. In , researchers reported that Immigration and Customs Enforcement uses facial recognition software against state driver's license databases, including for some states that provide licenses to undocumented immigrants.
In the Qingdao police was able to identify twenty-five wanted suspects using facial recognition equipment at the Qingdao International Beer Festival, one of which had been on the run for 10 years. That data is compared and analyzed with images from the police department's database and within 20 minutes, the subject can be identified with a
A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces, typically employed to authenticate users through ID verification services , works by pinpointing and measuring facial features from a given image. While initially 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 categorised as biometrics. Although the accuracy of facial recognition systems as a biometric technology is lower than iris recognition and fingerprint recognition , it is widely adopted due to its contactless process. Automated facial recognition was pioneered in the s.
Face recognition, as one of the most successful applications of image analysis, has recently gained significant attention. It is due to availability of feasible technologies, including mobile solutions. Research in automatic face recognition has been conducted since the s, but the problem is still largely unsolved. Last decade has provided significant progress in this area owing to advances in face modelling and analysis techniques. Although systems have been developed for face detection and tracking, reliable face recognition still offers a great challenge to computer vision and pattern recognition researchers.
PDF | This book discusses the major approaches, algorithms, and technologies used in automated face detection and recognition. Explaining.
The development of biometric applications, such as facial recognition FR , has recently become important in smart cities. Many scientists and engineers around the world have focused on establishing increasingly robust and accurate algorithms and methods for these types of systems and their applications in everyday life. FR is developing technology with multiple real-time applications.
In this paper, the algorithm of face recognition technology is made a comprehensive study. Firstly studied the methods of face detection, facial feature of bottom-up approach, template matching method, the method of face appearance, and then focused on color-based face detection algorithm. After studied method on face detection, the region segmentation of the face and the mark of facial feature are described.
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Face Detection and Recognition: Theory and Practice provides students, researchers, and practitioners with a single source for cutting-edge information on the.
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