  The way people look in terms of facial attributes (ethnicity, hair color,
facial hair, etc.) and the clothes or accessories they wear (sunglasses, hat,
hoodies, etc.) is highly dependent on geo-location and weather condition,
respectively. This work explores, for the first time, the use of this
contextual information, as people with wearable cameras walk across different
neighborhoods of a city, in order to learn a rich feature representation for
facial attribute classification, without the costly manual annotation required
by previous methods. By tracking the faces of casual walkers on more than 40
hours of egocentric video, we are able to cover tens of thousands of different
identities and automatically extract nearly 5 million pairs of images connected
by or from different face tracks, along with their weather and location
context, under pose and lighting variations. These image pairs are then fed
into a deep network that preserves similarity of images connected by the same
track, in order to capture identity-related attribute features, and optimizes
for location and weather prediction to capture additional facial attribute
features. Finally, the network is fine-tuned with manually annotated samples.
We perform an extensive experimental analysis on wearable data and two standard
benchmark datasets based on web images (LFWA and CelebA). Our method
outperforms by a large margin a network trained from scratch. Moreover, even
without using manually annotated identity labels for pre-training as in
previous methods, our approach achieves results that are better than the state
of the art.
