A Survey on Interdependent Privacy <s> Multimedia Data <s> Faces convey a wealth of social signals, including race, expression, identity, age and gender, all of which have attracted increasing attention from multi-disciplinary research, such as psychology, neuroscience, computer science, to name a few. Gleaned from recent advances in computer vision, computer graphics, and machine learning, computational intelligence based racial face analysis has been particularly popular due to its significant potential and broader impacts in extensive real-world applications, such as security and defense, surveillance, human computer interface (HCI), biometric-based identification, among others. These studies raise an important question: How implicit, non-declarative racial category can be conceptually modeled and quantitatively inferred from the face? Nevertheless, race classification is challenging due to its ambiguity and complexity depending on context and criteria. To address this challenge, recently, significant efforts have been reported toward race detection and categorization in the community. This survey provides a comprehensive and critical review of the state-of-the-art advances in face-race perception, principles, algorithms, and applications. We first discuss race perception problem formulation and motivation, while highlighting the conceptual potentials of racial face processing. Next, taxonomy of feature representational models, algorithms, performance and racial databases are presented with systematic discussions within the unified learning scenario. Finally, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potentially important cross-cutting themes and research directions for the issue of learning race from face. <s> BIB001 </s> A Survey on Interdependent Privacy <s> Multimedia Data <s> Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. In this paper we show that by learning representations through the use of deep-convolutional neural networks (CNN), a significant increase in performance can be obtained on these tasks. To this end, we propose a simple convolutional net architecture that can be used even when the amount of learning data is limited. We evaluate our method on the recent Adience benchmark for age and gender estimation and show it to dramatically outperform current state-of-the-art methods. <s> BIB002
Context. With mobile devices, including most modern smartphones and tablets, individuals can capture everyday life situations in the form of photos and videos (i.e., multimedia content). Such contents often feature individuals other than the individual who captured them, including family members, friends, friends of friends, colleagues, and strangers. And this content is often shared on OSNs (e.g., FB) or dedicated platforms (e.g., Flickr), possibly with (face) tags identifying the individuals featured in them. Unlike for other data types, with photos and videos, the data can be directly accessed by an attacker, hence he does not need to conduct any statistical inference. As such, photos and videos are multiple-subject data, not interdependent data (as defined in Section 2) per se. Therefore, the interdependent privacy risks associated with this type of data are mostly limited to the disclosure of the content itself. Yet, it is important to note that photos and videos can contain sensitive information about the individuals featured in them, including various personal and contextual attributes correlated with-thus inferable from-an individual's image or speech (e.g., gender and age BIB002 , ethnicity BIB001 , emotion, activity), and the fact that the individuals featured in the media are together, which reveals some information about their relationship  . In some cases, multimedia contents can also leak the location, and more generally the context, of the individuals featured in them.