How Computers Imagine Humans

João Martinho Moura (2017)

In the recent years, face detection technologies have been widely used by artists to create digital art. Face detection provides new forms of interaction and allows digital artifacts to detect the presence of human beings, through video capture and facial detection, in real-time. In this work, I explore the algorithm proposed by Paul Viola and Michael Jones, presented in 2001, sixteen years ago, in order to generate imagined faces from visual randomness.

How Computers Imagine Humans. João Martinho Moura (2016)

The first step of any face processing system is detecting the locations in images where faces are present (Yang, Kriegman, & Ahuja, 2002). In 2001, Paul Viola and Michael Jones introduced a machine learning approach for visual object detection which is capable of processing images extremely fast and achieving high detection rates (Viola & Jones, 2001). The Viola-Jones face detection algorithm is the first ever real-time face detection system in computer vision (Wang, 2014). OpenCV, a widely used free open-source library that enables computers to see (Bradski & Kaehler, 2008), implements a version of the Viola-Jones technique, which was extended by Rainer Lienhart and Jochen Maydt (Lienhart & Maydt, 2002). It is then used to detect objects in other images. These objects can be faces or others, like pedestrians or eyes. In my research, I use compulsive brute force requests into a well-known face detection algorithm, widely used in research and industry.

Having studied the Viola-Jones face detection algorithm, I got a very simple question: can a computer imagine and draw a human face, based only on that proposed algorithm?

I present an unusual use of the Viola-Jones technique, intended to do the opposite of what it is supposed to achieve: instead of trying to locate and capture faces, I generate facial images ‘imagined’ by a computer through the exploration of hypothetical possibilities.

Video

Video preview of the artwork (time was accelerated for this demonstration): 

More information about this project soon available.

2016-2017
João Martinho Moura

Research developed at:
School of Arts, Universidade Católica Portuguesa, Porto
CITAR – Centro de Investigação em Ciência e Tecnologia das Artes

References:

Yang, M. H., Kriegman, D. J., & Ahuja, N. (2002). Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1), 34–58.

Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 (Vol. 1, p. I-511-I-518). IEEE Comput. Soc. 

Wang, Y.-Q. (2014). An Analysis of the Viola-Jones Face Detection Algorithm. Image Processing On Line, 4, 128–148. 

Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library. OReilly Media Inc (Vol. 1). O’Reilly. 

Lienhart, R., & Maydt, J. (2002). An extended set of Haar-like features for rapid object detection. Proceedings. International Conference on Image Processing, 1, 900–903. 

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