Identify Visual Human Signature in community via wearable camera
Journal
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Journal Volume
2015-August
Pages
2229-2233
ISBN
9781467369978
Date Issued
2015
Author(s)
Abstract
With the increasing popularity of wearable devices, information becomes much easily available. However, personal information sharing still poses great challenges because of privacy issues. We propose an idea of Visual Human Signature (VHS) which can represent each person uniquely even captured in different views/poses by wearable cameras. We evaluate the performance of multiple effective modalities for recognizing an identity, including facial appearance, visual patches, facial attributes and clothing attributes. We propose to emphasize significant dimensions and do weighted voting fusion for incorporating the modalities to improve the VHS recognition. By jointly considering multiple modalities, the VHS recognition rate can reach by 51% in frontal images and 48% in the more challenging environment and our approach can surpass the baseline with average fusion by 25% and 16%. We also introduce Multiview Celebrity Identity Dataset (MCID), a new dataset containing hundreds of identities with different view and clothing for comprehensive evaluation. ? 2015 IEEE.
Subjects
Human Attributes; Visual Human Signature; Wearable Device; Weighted Voting
Other Subjects
Audio signal processing; Cameras; Data privacy; Speech communication; Comprehensive evaluation; Facial appearance; Human attributes; Human signatures; Multiple modalities; Personal information; Wearable devices; Weighted voting; Wearable technology
Type
conference paper