Name
Abstract | ||
|---|---|---|
Smartphones represent powerful mobile computing devices enabling a wide variety of new applications and opportunities for human interaction, sensing and communications. Because smartphones come with front-facing cameras, it is now possible for users to interact and drive applications based on their facial responses to enable participatory and opportunistic face-aware applications. This paper presents the design, implementation and evaluation of a robust, real-time face interpretation engine for smartphones, called Visage, that enables a new class of face-aware applications for smartphones. Visage fuses data streams from the phone's front-facing camera and built-in motion sensors to infer, in an energy-efficient manner, the user's 3D head poses (i.e., the pitch, roll and yaw of user's heads with respect to the phone) and facial expressions (e.g., happy, sad, angry, etc.). Visage supports a set of novel sensing, tracking, and machine learning algorithms on the phone, which are specifically designed to deal with challenges presented by user mobility, varying phone contexts, and resource limitations. Results demonstrate that Visage is effective in different real-world scenarios. Furthermore, we developed two distinct proof-of-concept applications, Streetview+ and Mood Profiler driven by Visage. © 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-36632-1_9 | Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering |
Keywords | Field | DocType |
face interpretation engine,face-aware mobile application | Mobile computing,Data stream mining,Computer security,Computer science,Human interaction,Facial expression,Phone,Motion sensors,Fuse (electrical),Multimedia | Conference |
Volume | Issue | ISSN |
110 LNICST | null | null |
Citations | PageRank | References |
6 | 0.46 | 26 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaochao Yang | 1 | 109 | 5.75 |
| Chuang-wen You | 2 | 254 | 34.68 |
| Hong Lu | 3 | 2730 | 150.65 |
| Mu Lin | 4 | 229 | 10.94 |
| Nicholas D. Lane | 5 | 4247 | 248.15 |
| Andrew T. Campbell | 6 | 8958 | 759.66 |