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Berkeley COMPSCI 294 - iFace – Discreet Mobile Cameras

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iFace – Discreet Mobile CamerasEECS 294-06 Vision Class Project, UC Berkeley, fall 2006Ali AmirmahaniBonnie ZhuAbstract:As the usage of mobile hand-held devices with embedded camera becomes more popular,people are more willing to explore its functionality as video conferencing tool or alike. However, it invokes privacy concerns such as involuntary involvement of people in the background and user’s unintended disclosure of background information. In this project, we aim to create a iFace – a privacy discreet functionality, to identify and track user’s face in dynamic environment as user’s moving; furthermore, to blur out the background and display user’s face only.Introduction:It’s a prevailing trend that more and more people are adopting the usage of mobile hand-held devices with built in cameras, such as mobile phone, PDA (personal digital assistant), and iPOD with video. The multimedia capacity not only enriches communication experience such as live-video-chat but facilitates collaborations over geo-distance including teleconferencing and tele-clinic etc. Young people, especially teenagers, enjoy live chat among them. Mobile devices equipped with camera enable them to converse, aimed with facial images while on the go. For possibility beyond simple entertaining purpose, imagine in a nursing home,a nurse1 checks rounds with a mobile phone and PDA with her, if she sees something 1 Assume the nurse is a female for notation convenience.abnormal happening to an elderly, she can connect to a physician at a remote location with the elderly’s image transmitted for rudimentary first-hand diagnose before further action needs to be taken. However, this also raises some privacy issues. The first category arises from the concernsof those who are in the background while the live video taking place. They have their rights and privacy to be not included in the video stream. The second category of concerns is the involuntary disclosure of user’s background information even without others presence. What if, as a teenager, I don’t like my messy dorm being displayed to my friends while I am chatting with them; or as a businessman, I don’t want my other business partner, who’s on the video conference with me, to see the specific stores I am shopping from etc and leak certain business secretes. There’s a reasonable need from both the user and others involuntarily involved to be able to opt out the disclosure of background images. Then can the mobile camera be discreet enough to only capture an intended user’s face but nothing more?In this project, we study and implement a solution to this problem by detecting and tracking the user’s face while blurring out the background. The structure of this paper goes the following, section 1 states some technical challenge that this problem poses; section 2 outlines both the specific steps and the algorithm of solving this problem; section 3 addresses integration consideration including choice on hardware, operating system and software; section 4 provides analysis on the results of theexperiments we have carried out; section 5 concludes our current work and shows possible future work we are planning to extend.I. Problem setup. The challenges of creating a functionality for discreet mobile camera lies in the following areas,1. Both the background and foreground are moving as the user hold the handhelddevice with camera on board on the go. This is different from mounting a camera at a fixed location so that a simple background subtraction does not work. 2. The camera has to track the face in a dynamic environment as both the trajectory of the face and the background images evolve over time. 3. There are very limited device resource can be dedicated to this rather expansive image processing functionality as mobile devices have very limited memory and computation power. 4. Operating systems such as Mobile Windows, Linux and Palm have different tradeoffs on performance and speed. II. Specific steps and algorithm We divide the problem into following steps,a. Simulation. We use Open CV library to run simulations on a IBM ThinkPad T40 with Dell(?) WebCam first. b. Face - detection. We specify definitions of both foreground and background by using color histogram and initialize with Adaboost, Haar strong classifiers. The weak classifier is trained with a library of thousandsof faces to become a strong classifier. More details are elaborated in section 3. c. Face – tracking. A mean shift algorithm will be implemented to track the moving face.d. Background – blurring. Some basic averaging and smoothing techniquesare used to achieve blurring.e. Eventually, we will port the code to a PDA, HP iPAQ hw6500 to be specific. The detailed list of routines in Open CV library being called are depicted in followingdiagram. III. Integration ConsiderationIn order to implement the algorithm, we need to properly choose hardware, operating system and software, coordinately for our iFace. a. Hardware i. Architecture differences between PocketPC & x86 .Since our goal is to port all related code to PDA, it’s critical to understand the hardware architectures first, which we learned through a hard way. PocketPC and regular PC box are built on ARM (Advanced RISC Machine) and x86, respectively.The ARM, a low-cost and power-efficient 32-bit RISC (Reduced Instruction Set Computer) microprocessor, is in use in 75% of 32-bit embedded CPUs2. ARM’s dominance in current market furbishes iFace with suitability for a vast pool of users. The architecture difference between PocketPC and regular PC box, thus the emulator, requires us to develop two versions of embedded Visual C++ 4.0 for both the emulator and the actual device. ii. Camera settingAn ideal PDA solution is to have a swivel camera like the Sony Clie PEG-NX70V, which can face towards and/or away the viewer of a PDA screen. This hardware feature is ideal for both transmitting and viewing the video, which currently runs on PlamOS 53. b. Operating System – Long term technical support.The primary operating systems in mobile handheld devices with built-in camera are the following,2 These portable devices include PDAs, mobile phones, XScale by Intel and OMAP by Texas Instruments. 3 Unfortunately, as we stated in later section, that Palm does not offer consistent technical support due to business reasons of product lines.i. BlackBerry, which runs on BlackBerry and RIM lines of PDAs


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Berkeley COMPSCI 294 - iFace – Discreet Mobile Cameras

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