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Face Detection in Distributed Camera Sensor Networks

Face Detection in Distributed Camera Sensor Networks. Rajapaksage Jayampathi DM Rasanjalee Himali. OUTLINE . Introduction Face Detection Distributed Camera Sensor Networks (DCS) Current Work Viola-Jones object detection framework Current Limitations: Proposed Work

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Face Detection in Distributed Camera Sensor Networks

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  1. Face Detection in Distributed Camera Sensor Networks Rajapaksage Jayampathi DM Rasanjalee Himali

  2. OUTLINE • Introduction • Face Detection • Distributed Camera Sensor Networks (DCS) • Current Work • Viola-Jones object detection framework • Current Limitations: • Proposed Work • Distributed Face Detection Framework • Implementation • Future Plan

  3. 1.INTRODUCTION

  4. Face Detection • Acomputer technology that determines the locations and sizes of human faces in arbitrary (digital) images. • It detects facial features and ignores anything else, such as buildings, trees and bodies. • Is a specific case of object-class detection • Many algorithms implement the face-detection task as a binary pattern-classification task

  5. Face Detection [Contd.] • In our work we focus specially on the problem of face detection in still images. • The most straightforward variety of this problem is the detection of a single face at a known scale and orientation. • Even this, is a nontrivial problem. • The most immediate application that comes to mind for face detection is as the first step in an automated face recognizer

  6. Distributed Smart Cameras (DSCs) • Are real-time embedded systems that achieve computer vision using multiple cameras. • One of the basic and most important problems of smart camera networks is face detection • A smart camera consists of sensing, processing, and communication units which deliver some abstracted data of the observed scene. • They perform a verity of image processing algorithms • Ex : motion detection, segmentation, tracking, and object recognition and delivers color and geometric features, segmented objects or high level decisions as output

  7. Distributed Smart Cameras (DSCs) [Contd.] • The main goal for the cameras is to provide sufficient processing power and fast memory for processing the images in real time while keeping power consumption low • DSCs introduce distribution and collaboration to smart cameras. • These cameras use distributed algorithms to perform camera operations. • Multiple threads of processing may take place on different processing nodes in parallel. • These camera sensors generate more data and make analysis difficult in many applications.

  8. Distributed Smart Cameras (DSCs) [Contd.] • Distributed smart cameras distribute not only sensing but also processing • On the one hand, smart cameras can serve as processing nodes that perform some fixed preprocessing but still deliver data to a central server. • On the other hand, processing may be organized in a completely decentralized fashion where the smart cameras organize themselves and collaborate dynamically.

  9. Smart Cameras in Sensor Networks

  10. 2. Current Work

  11. Current Work • There are various solutions to this problem • Most of which deal with faces at arbitrary scales, and assume an upright face • Most methods use a learning algorithm on a training set to begin the detection process.

  12. Robust Real-Time Face Detection [Viola & Jones ,2004] • capable of processing images extremely rapidly while achieving high detection rates • Integral Image: • Introduces a new image representation called an integral image that allows for very fast feature evaluation • The integral image can be computed from an image using a few operations per pixel. • Once computed, any one of these Haar-like features can be computed at any scale or location in constant time.

  13. Robust Real-Time Face Detection [Viola & Jones ,2004] • The integral image at location x, y contains the sum of the pixels above and to the left of x, y, inclusive: • where ii (x, y) is the integral image and i (x, y) is the original image • Using the following pair of recurrences: • (where s(x, y) is the cumulative row sum, s(x,−1) = 0, and ii (−1, y) = 0) the integral image can be computed in one pass over the original image.

  14. Robust Real-Time Face Detection [Viola & Jones ,2004] [Contd.] • Uses a simple and efficient classifier that is built by selecting a small number of important features from a huge library of potential features using AdaBoost • Combine successively more complex classifiers in a cascade structure which dramatically increases the speed of the detector by focusing attention on promising regions of the image • The final detector is scanned across the image at multiple scales and locations. Scaling is achieved by scaling the detector itself, rather than scaling the image.

  15. Features

  16. Disadvantages of Current Work • many algorithms are centralized algorithms and are not designed for distributed or resource constrained environments. • There are only a handful of parallel architectures for face detection have been proposed in the literature so far. • None of these take into consideration the multiple views different cameras may have due to its relative position in a global 3-D coordinate system. • Many current approaches assume up-right faces although few algorithms have been devised to address multi-view face problem. • Viola & Jones[2] approach limits itself to a limited set of features and classifiers to reduce computation.

  17. 3. Our Approach

  18. Problem Statement • Our work is an extension of the face detection algorithm proposed by Viola-Jones. • The major distribution of load is contributed by two factors: • Computation of integral image and the • Face detection. • The objective of this distributed face detection framework is to achieve higher detection rates at low false positive rates by using the power of distributed computing in a DSC network.

  19. Our Approach • The advantage of distributed computing is achieved at three stages: • Integral Image Computation • Integral Image Distribution and • Cascade and Feature Distribution

  20. Si Sj Sk (a) (b) (c) 1. Integral Image Computation • The Integral image calculation can be distributed among cameras by formulating the problem as a parallel prefix sum calculation problem • Given the original image at a sensor s, it can partition the image at subwindows to its neighbors. The most intuitive way will be to do a row-wise partitioning. • The neighbors will calculate the partial-integral image for the given subwindow and send back to s. s calculates the global integral image

  21. Partial Integral image Partial Integral image Partial Integral image Partial Integral image 3. Calculate Partial integral Image 2. Distribute image sub windows to Nodes SENSOR B Image Sub Window SENSOR C 1. Receive Image SENSOR D Original Image SENSOR E 4.Gather Partial Integral Images Image Sub Window Combined Partial Integral Image 5.Calculate global Integral Image Global Integral Image SENSOR A PARALLEL PREFIX SUM

  22. 2. Integral Image Distribution • A better approach however is to incrementally calculate integral image at sensor which received the original image while distributing it to different nodes for face detection. • Each incremental version of integral image corresponds to a different scale and

  23. Detect Faces A B A SENSOR B Distribute integral Image Integral Image Detect Faces SENSOR A SENSOR C The detection process for different scales are completed by neighborhood sensor nodes concurrently. Detect Faces SENSOR D

  24. 3. Cascade and Feature Distribution • The Viola Jones algorithm limits itself to a limited set of features for faster results. • However, in a distributed environment like DSC network, larger number of feature set can be used without compromising the faster face detection. • Also, the pipeline architecture of cascades can be implemented in a distributed environment by assigning set of adjacent classifiers in the cascade to sensors.

  25. Detect Faces SENSOR B F F F T T 1 T 2 3 Detect Faces SENSOR X SENSOR Y SENSOR Z Attentional Cascade SENSOR C Detect Faces SENSOR D

  26. MASTER NODE PROCESS Wait For Image reception Original Image Received Image? Unused Neighbor? Yes Row-wise partition image Image Sub-window Yes No Partial Integral Image No Received ALL partial Integral Images? Yes Calculate Global Integral Image Received ALL Face Detected Sub-Window Unused Neighbor Yes No Row-wise partition Integral image No Yes Face Detected Sub-window Integral Image Sub-window

  27. SLAVE NODE PROCESS Wait For Image reception Partial Image Sub-Window Received Partial Image Sub Window? No Yes Received Partial ii? Calculate Partial Integral Image Yes Send Partial ii to MASTER Detect Faces Send Face detected ii to MASTER

  28. 4. Future Work

  29. Future Work • It is possible to devise a distributed face detection algorithm in DSC networks incorporates multi-view face detection in DSC networks. • Use Value of Information theory to detect faces reliably.

  30. References • Multi-Camera Networks: Principles and Applications, Hamid Aghajan, Andrea Cavallaro, 2009 • Robust Real-time Object Detection, Paul Viola , Michael Jones, 2001 • Parallelized architecture of multiple classifiers for face detection, Bridget B. Jung Uk Cho ,IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP) , 2009 • CMUcam3: An Open Programmable Embedded Vision Sensor , Anthony Rowe, Adam Goode, Dhiraj Goel, Illah Nourbakhsh, , Carnegie Mellon Robotics Institute Technical Report, RI-TR-07-13 May 2007 • Fast Multi-View Face Detection, M. Jones, P. Viola, MERL, TR2003-96, July 2003 • Robust Multi-View Multi-Camera Face Detection inside Smart Rooms Using Spatio-Temporal Dynamic Programming, Z. Zhang, G. Potamianos, M. Liu, T. Huang, In Proceedings of the International Conference on Automatic Face and Gesture Recognition, pp.407-412, 2006 • Robust Real-Time Face Detection, P. Viola and M. Jones, International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004. • Towards a Real-time and Distributed System for Face Detection, Pose Estimation and Face-related Features , J. Nesvadba, A. Hanjalic, P. M. Fonseca1, B. Kroon, H. Celik, E. Hendriks, Int. Conf. on Methods and Techniques in Behavioral Research, 2005 • A statistical method for 3D object detection applied to faces and cars, Schneiderman, H. and Kanade, T, In International Conference on Computer Vision, 2000 • Neural network-based face detection, Rowley, H., Baluja, S, and Kanade, T, IEEE Patt. Anal. Mach. Intell, 1998 • Dual camera system for face detection in unconstrained environments Marchesotti, L.   Marcenaro, L.   Regazzoni, C.   DIBE, Genoa Univ., Italy, ICIP, 2003

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