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Abstract

This chapter discusses the use of facial recognition technology (FRT) by law enforcement with a special focus on machine learning applications. First, FRT is situated in the larger context of the rise of video surveillance and a shift in Western security culture. The chapter shows how the promise of FRT addresses the problems of video surveillance. Then, it explains how FRT works and how machine learning techniques such as eigenface and convolutional neural networks come into play. Afterward, it explains how the accuracy and effectiveness of FRT are evaluated and why it is important to take the interplay of human operators and FRT systems into account. Finally, several controversies around the police use of FRT are presented and the limits of technological solutions to the problem of bias are demonstrated.

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Most cited references 57

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Deep learning.

Yann LeCun, Yoshua Bengio, Geoffrey E Hinton (2015)

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.