  Person re-identification (re-ID) in first-person (egocentric) vision is a
fairly new and unexplored problem. With the increase of wearable video
recording devices, egocentric data becomes readily available, and person
re-identification has the potential to benefit greatly from this. However,
there is a significant lack of large scale structured egocentric datasets for
person re-identification, due to the poor video quality and lack of individuals
in most of the recorded content. Although a lot of research has been done in
person re-identification based on fixed surveillance cameras, these do not
directly benefit egocentric re-ID. Machine learning models trained on the
publicly available large scale re-ID datasets cannot be applied to egocentric
re-ID due to the dataset bias problem. The proposed algorithm makes use of
neural style transfer (NST) that incorporates a variant of Convolutional Neural
Network (CNN) to utilize the benefits of both fixed camera vision and
first-person vision. NST generates images having features from both egocentric
datasets and fixed camera datasets, that are fed through a VGG-16 network
trained on a fixed-camera dataset for feature extraction. These extracted
features are then used to re-identify individuals. The fixed camera dataset
Market-1501 and the first-person dataset EGO Re-ID are applied for this work
and the results are on par with the present re-identification models in the
egocentric domain.
