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1,001,001 faces : a configural face navigation interface

University of British Columbia

Conventional face navigation systems focus on finding new faces via facial features. Though intuitive, this method has limitations. Notably, it is geared toward finding distinctive features, and hence, does not work as effectively on "typical" faces. We present an alternative approach to searching and navigating through an overall face configuration space. To do so, we implemented an interface that shows gradients of faces arranged spatially using an n-dimensional norm-based face generation method. Because our interface allows users to observe faces holistically, facial composition information is not lost during searching, an advantage over face component methods. We compare our gradient based face navigation system with a typical, static, slider-based system in a navigation task. Then we compare it with a hybrid dynamic slider system. Results from our first pilot study show that our method is more effective at allowing users to concentrate on face navigation when compared with a static slider interface. This is helpful for face matching tasks as it reduces the number of times users must re-examine faces. Results from our second pilot study suggest that our interface is slightly more effective in coping with correlated navigation axes when compared with a dynamic slider interface. Our third pilot and the formal experiment confirm that while slider-based interfaces are more suited for converging to proximity to the target face, gradient-based interfaces are better for refinement. While it may be counter-intuitive that sliders, which are commonly used as interfaces for colour navigation, are inadequate for face matching tasks, our results suggest that new interfaces, such as our gradient-based system and dynamic sliders, are useful for navigation in higher dimensional face space.

Thesis/Dissertation

application/pdf

2009-10-29

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http://hdl.handle.net/2429/14355

Computer Science

University of British Columbia

UBCV

DSpace