Biblio

The emphasis on exhaustive passive capturing of images using wearable cameras like Autographer, which is often known as lifelogging has brought into foreground the challenge of preserving privacy, in addition to presenting the vast amount of images in a meaningful way. In this paper, we present a user-study to understand the importance of an array of factors that are likely to influence the lifeloggers to share their lifelog images in their online circle. The findings are a step forward in the emerging area intersecting HCI, and privacy, to help in exploring design directions for privacy mediating techniques in lifelogging applications.

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.