Editing Facial recognition system (section)

=== Traditional ===
[[Image:eigenfaces.png|thumb|Some [[eigenface]]s from [[AT&T Labs|AT&T Laboratories]] Cambridge]]
Some face recognition [[algorithms]] identify facial features by extracting landmarks, or features, from an image of the subject's face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw.<ref>{{cite web|url=http://www.hrsid.com/company/technology/face-recognition|title=Airport Facial Recognition Passenger Flow Management|work=hrsid.com}}</ref> These features are then used to search for other images with matching features.<ref name="Bonsor2">{{cite web|url=http://computer.howstuffworks.com/facial-recognition.htm|title=How Facial Recognition Systems Work|last=Bonsor|first=K.|access-date=June 2, 2008|date=September 4, 2001}}</ref>

Other algorithms [[normalization (image processing)|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.<ref name="Smith2">{{cite web|url=http://www.biometrics.gov/Documents/FaceRec.pdf|title=Face Recognition|last=Smith|first=Kelly|access-date=June 4, 2008}}</ref> One of the earliest successful systems<ref>R. Brunelli and T. Poggio, "Face Recognition: Features versus Templates", IEEE Trans. on PAMI, 1993, (15)10:1042–1052</ref> is based on template matching techniques<ref>R. Brunelli, ''Template Matching Techniques in Computer Vision: Theory and Practice'', Wiley, {{ISBN|978-0-470-51706-2}}, 2009 ''([http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470517069.html] TM book)''</ref> applied to a set of salient facial features, providing a sort of compressed face representation.

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.<ref>{{Cite book|title=Advances in Biometrics: International Conference, ICB 2006, Hong Kong, China, January 5–7, 2006, Proceedings|last1=Zhang|first1=David|last2=Jain|first2=Anil|publisher=Springer Science & Business Media|year=2006|isbn=9783540311119|location=Berlin|pages=183}}</ref>

Popular recognition algorithms include principal component analysis using [[eigenface]]s, [[linear discriminant analysis]], [[elastic matching|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]].{{citation needed|date=September 2020}}<ref>{{Cite journal|title=A Study on the Design and Implementation of Facial Recognition Application System|journal=International Journal of Bio-Science and Bio-Technology}}</ref> Modern facial recognition systems make increasing use of machine learning techniques such as [[deep learning]].<ref>H. Ugail, ''Deep face recognition using full and partial face images'', Elesevier, {{ISBN|978-0-12-822109-9}}, 2022 ''([https://doi.org/10.1016/B978-0-12-822109-9.00015-1] Advanced Methods and Deep Learning in Computer Vision)''</ref>