This document proposes a privacy protection scheme that deteriorates the visual quality of the human eye region in real-time video streams. It consists of an automatic eye detection module using skin segmentation and generative framework based detection, and a JPEG XR scrambling module that encrypts transform coefficients in the located eye region. Experimental results show that using a 2.4d x 1.2d eye region block size significantly reduces the face recognition rate of two algorithms on the CMU PIE database, while maintaining low encoding delay per frame.

1. A Novel Eye Region Based Privacy Protection Scheme
Dohyoung Lee, Konstantinos N. Plataniotis
Multimedia Lab., The Edward S. Rogers Sr. Department of Electrical and Computer Engineering
University of Toronto, Toronto, Ontario, Canada M5S 3G4
1. Introduction 3.2. Generative framework based eye detection 5.2. Results
• Haar-like feature / GentleBoost learning based object detection in two stages [1] • Examples of protected sample images
1.1. Privacy Protection of Video Contents and Eye Region
 Stage 1 : Facial region detection from grayscale version of input frame
• Proliferation of video surveillance systems and human behavior researches has
 Stage 2 : Eye region detection from the detected facial region
raised a concern of revealing the identity in video data via face recognition software
• Customizations for enhanced eye detection performance
• Why Protect Eye Region ?
 Apply the binary skin map from segmentation stage to reduce search delay
 Conceal the region containing the most discriminative information within face
 Incorporate a novel RGB-grayscale conversion to improve detection accuracy
 Reduce computational costs compared to the entire facial region approach
 Thwart face recognition tools which typically rely on eye detection as an
essential preprocessing step
1.2. Contribution
Proposed a video privacy protection solution that effectively deteriorates the visual
quality of human eye region in real time, while producing stream compatible to a 4. JPEG XR scrambling module
worldwide JPEG XR compression standard • Cost-effective video encryption tool embedded in JPEG XR encoding module [2]
• Apply different encryption techniques to transform coefficients of JPEG XR Fig.4. Eye region scrambling results on test images from CMU PIE database
frequency subbands in located eye region
• Face recognition rates for various eye region block sizes and invasion scenarios
2. System Overview
• Consist of an automatic
eye detection module and a
JPEG XR scrambling
module
• Simplified architecture that
5. Experiment
processes each frame 5.1. Methodology
independently from the • Two benchmark recognition algorithms used to validate effectiveness of solution
previous frame  Principal Component Analysis with nearest-neighbor classifier (PCA-NN)
 Local Binary Pattern feature with nearest-neighbor classifier (LBP-NN)
• Produce JPEG XR intra- • Database : color frontal face images of from CMU PIE database
coded video stream with  Wide range of facial variations in illumination condition and ethnic groups
6. Conclusion
scrambled eye region  68 gallery, 340 training, and 1710 test images normalized to 192x192 size • The proposed scheme with 2.4d x 1.2d eye region block size significantly reduces
• Experimental setup for eye region block sizes and invasion scenarios successful identification rate of two widely used face recognition algorithms
• Encoding delay per 192x192 frame is 34.94 ms for the proposed solution and
46.20 ms for the facial region solution (on Core2 Duo 2.53GHz CPU with 4GB
Fig.1. Overview of the proposed eye region protection solution RAM running Windows 7)
• Results indicate that the proposed eye region based scheme effectively removes
Fig.2. Scrambling block size represented with respect to eye distance d discriminative features in facial region with reduced computational costs
3. Automatic Eye Detection Module
Locate the eye region in real-time from input video frame using:
 Color based skin segmentation
 Generative framework based eye detection
3.1. Skin-tone Segmentation 7. References
• Generate a binary skin map to reduce face search region of subsequent module
• Segmentation performed in both YCbCr and HSV color spaces to compensate the [1] I. Fasel, B. Fortenberry, and J. Movellan, “A generative framework for real
unreliability of the single color space approach time object detection and classification,” Comput. Vis. Image Underst., vol. 98, pp.
182–210, Apr. 2005
Fig.3. Privacy invasion scenarios [2] H. Sohn, W. De Neve, and Y. Ro, “Privacy protection in video surveillance
 Scenario 1 : altered test images, non-altered gallery and training images systems: Analysis of subband-adaptive scrambling in JPEG XR”, IEEE Trans. on
 Scenario 2 : altered test images, altered gallery, and training images Circuits Syst. for Video Technol., vol. 21, no. 2, pp. 170 –177, Feb. 2011