Face (Re)Cognition

Faces are funny things. They are inordinately plastic and expressive, constantly changing according to our health, attitudes and moods—brightening and darkening, lifting and drooping, opening and closing—and always, inexorably in the process of ageing, whatever makeup or makeover manoeuvres we try to pull off.

Despite this plasticity, we are said to be able to recognise faces intuitively, calling on our hard-wired, animalistic and perhaps atavistic ability to hone in on the generic (age, race, sex etc.) and ultimately specific identity of whoever is in our sights. Not surprisingly, scientists and technologists attempt to grasp this ability, tending to regard it as something biologically determined and universal, while others highlight significant cultural differences and the existence of particular ways of seeing. The science and technology of face recognition generates automated systems that allegedly not only match, but supersede human ability, and at the same time, it elides ways of seeing that were established during the industrial revolution in Western Europe.

Facial recognition technology (FRT) is a form of biometrics that, along with iris scanning and fingerprinting, adheres to the principle of indexicality, namely, the objective representation or even symbolic presence of the object – finger, iris, face – in an image. In this case, the image is twice removed from the object; a photograph of a photograph if you like, or a further digitisation of a digital image of a face. The aim of a facial recognition system is to either verify or identify someone from a still or video image. Following the acquisition of this ‘probe’ image, the system must first of all detect the face or distinguish between the face and its surroundings (easy for us, but not for computers). To do this it selects certain landmark features, such as the shape of the eyes and size of the nose, in order to compare them with the database. Either that or it generates what are called standard feature templates – averages or types. Once detected, the face is normalised or rather, the image is standardised with respect to lighting, format, pose and so on. Again, this aids comparison with the database. However, the normalisation algorithm is only capable of compensating for slight variations, and so the probe image must already be ‘as close as possible to a standardized face’. In order to facilitate face recognition, the already standardised image is translated and transformed into a simplified mathematical representation called a biometric template. The trick, in this process of reductive computation, is to retain enough information to distinguish one template from another and thereby reduce the risk of creating ‘biometric doubles’.

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Published in Photoworks Issue 17, 2011
Commissioned by Photoworks

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