X-Faces The eXploit Is Out There



In the combinatorial form of creativity novel ideas are produced through unfamiliar combinations of familiar ideas. We explore this type of creativity in the scope of Data Augmentation applied to Face Detection. Typically, the creation of face detectors requires the construction of datasets of examples to train, test, and validate a classifier, which is a troublesome task. We propose a Data Augmentation technique to autonomously generate new frontal faces out of existing ones. The elementary parts of the faces are recombined using Evolutionary Computation and Computer Vision techniques. The key novel contributions include: (i) an approach capable of automatically creating face alternatives; (ii) the creation and usage of computational curators to automatically select individuals from the evolutionary process; and (iii) an experimentation with the interplay between Data Augmentation and serendipity. The system tends to create a wide variety of unexpected faces that exploit the vulnerabilities of face detectors. The overall results suggest that our approach is a viable Data Augmentation approach in the field of Face Detection.


Proceedings of the seventh International Conference on Computational Creativity (ICCC), June 2016

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