MACHINE VISION GROUP Head-tracking virtual 3-D display for mobile devices Miguel Bordallo López*, Jari Hannuksela*, Olli Silvén* and Lixin Fan**, * University of Oulu, Finland ** Nokia Research Center, Tampere, Finland

MACHINE VISION GROUP Contents Introduction Head-tracking 3D virtual display Interaction design Face-tracking for mobile devices Mobile device’s constrains Field of view Energy efficiency Implementation Latency considerations Performance Summary

MACHINE VISION GROUP Introduction 3D virtual displays Calculate the relative position of the user respect to the screen Calculate the angle of the user’s point of view Render an image according to the point of view Result is a Virtual Window: - Shows realistic 3D objects - Based on parallax effect * Video from Johnny Lee (Wiimote head tracking project) The position information is used to render the 3D UI/content as if the user watched it from different angles. The technology enable users to watch the content from different angles and become more immersed.

MACHINE VISION GROUP Introduction Mobile 3D virtual displays Mobile head-coupled display can take advantage of the small size Movement of either user or device Mobile Devices have cameras and sensors integrated No need for external periferics Can increase UI functionalities New applications and concepts Realistic 3D objects can be rendered and perceived New interaction methods can be developed We know what the user looks at and we can use that information

MACHINE VISION GROUP Demo

MACHINE VISION GROUP Head-tracking mobile virtual 3D display A simple use case

MACHINE VISION GROUP Interaction design

MACHINE VISION GROUP Head-coupled displays require robust and fast face-tracking Based on multiscale LBP, Cascade classifier and AdaBoost Excellent results in face recognition and authentication, face detection, facial expression recognition, gender classification Introduction Mobile face-tracking

MACHINE VISION GROUP Introduction Evaluating the distance to the screen Essential to compute de relative angle Ground truth determined With Kinect Two methods evaluated: Face size obtained with face tracking Flickering between frames No extra computations needed Good accuracy Motion estimation library: Harris corners + BLUE Computes changes of scale between frames Presents about 10% more accuracy Less flickering between frames Needs extra computations: Introduces latency, decreases framerate Worse input sequence for tracking More differences between frames

MACHINE VISION GROUP Mobile constrains Field of view Front Camera is on the device’s corner and not pointing to the user: Reduced field of view (<45dg) Assymmetric FoV Even more reduced effective FoV Considerable minimum distance to the screen User often outside of the point of view Tracking sometimes lost Need to show viewfinder on the screen

MACHINE VISION GROUP Mobile constrains Field of view Implemented solution: Wide angle lens Dramatically increases the effective field of view (<160dg) Requires calibrated lens Requires de-warping routine Implemented with lookup tables Problems when several faces are on the field of view

MACHINE VISION GROUP Mobile constrains Energy efficiency Practical challenge of camera-based UI is to have an always active camera Lower framerate -> High UI starting latencies Higher framerate -> Small energy-efficiency Application processor (even in mobile) is power hungry Specific processors closer to the sensors are needed Current devices include HW-codecs and GPUs: Better energy efficiency due to small EPI Mobile GPU already programable: OpenGL ES OpenCL Embeded Porfile

MACHINE VISION GROUP Energy efficiency GPU-accelerated face-tracking Computational and energy costs per VGA frame of feature extraction GPU can be treated as an independent entity Can be use concurrently with CPU Use of GPU for feature extraction (format conversion + multiscaling + LBP) Mobile GPUs still not very efficient for certain tasks

MACHINE VISION GROUP Implementation Demo platform: N900 (Qt + Gstreamer + openGL ES) Based on face-tracking external library Implementation details: Input image resolution : 320x240 Frame rate: fps. Base latency: ms. Accepted field of view: < 45dg hori. & < 35dg vert. User’s distance range: cm.

MACHINE VISION GROUP Implementation Simple block diagram

MACHINE VISION GROUP Implementation Task distribution

MACHINE VISION GROUP Implementation Task distribution Camera moduleApplication Processor CPU Graphics Processor GPU Touchscreen Display

MACHINE VISION GROUP Implementation Task distribution Camera moduleApplication ProcessorGraphics ProcessorTouchscreen

MACHINE VISION GROUP Mobile constrains Latency User interface latency is a critical issue Latency > 100ms. Very disturbing Realistic 3D rendering even more sensitive Not realistic if it happened a while ago !!!

MACHINE VISION GROUP Mobile constrains Latency hiding A possible solution: Latency hiding Requires good knowledge of the system’s timing Predict the current position based on motion vector

MACHINE VISION GROUP Performance Demo platform: Nokia N900 ARM cortex A8, 600 MHz + PowerVR535 GPU Comparison platform: Nokia N9 ARM cortex A8, 1 GHz + PowerVR535 GPU

MACHINE VISION GROUP Remaining problems Face-tracking based 3D User Interfaces provide support for new concepts Face tracking can be offered as a platform level Current mobile platforms still present several shortcomings Energy efficiency compromises battery life Camera not designed for UI purposes Single camera implies difficult 3D context recognition

MACHINE VISION GROUP Thank you Any question?

MACHINE VISION GROUP LBP fragment shader implementation Access the image via texture lookup Fetch the selected picture pixel Fetch the neighbours values Compute binary vector Multiply by weighting factor Uses OpenGL ES interface Two versions: –Version 1: calculates LBP map in one grayscale channel –Version 2: calculates 4 LBP maps in RGBA channels

MACHINE VISION GROUP Preprocessing Create quad Divide texture & Convert to grayscale Render each piece in one channel

MACHINE VISION GROUP GPU assisted face analysis process