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ForSe Overview Forensics and Security Laboratory (ForSe Lab) School of Computer Engineering Nanyang Technological University.

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Presentation on theme: "ForSe Overview Forensics and Security Laboratory (ForSe Lab) School of Computer Engineering Nanyang Technological University."— Presentation transcript:

1 ForSe Overview Forensics and Security Laboratory (ForSe Lab) School of Computer Engineering Nanyang Technological University

2 2 Mission of ForSe Lab  To create a synergistic group dedicated to research in the application of computational techniques to biometrics, information security and forensic analysis.  To perform cutting edge research and train and develop talents to support Singapore’s efforts in the areas of Homeland Security and Infocomm Security.  To make use of strong research base to further enhance the research contributions from NTU to the international arena in the areas of forensic and security.

3 3 Vision of ForSe lab  To be one of the major research labs/centres for research and development in the areas of forensics, biometrics, and security technologies.  To be a strong research arm between academic and industry to support R&D activities in forensics and security for Singapore.

4 4 Facts and Figures  Established in late 2005.  6 active faculty members, 1 research assistant, 1 lab executive, 11 PhD students.  6 funded projects with total amount over S$500K.  Supports approximately 10-15 Final Year Projects every academic year

5 5 Research Funding Award  InfoComm Research cluster, NTU  Institute for Infocomm Research (I2R) – Joint Collaboration Project with I2R  Three Academic Research Fund Tier 1, Ministry of Education, Singapore  MINDEF-NTU Joint R&D project

6 6 Active Members David Cho (Asst Prof) Director Maylor Leung (Assoc Prof) Vinod Prasad (Asst Prof) Adams Kong (Asst Prof) Sudha Natarajan (Asst Prof) Li Fang (Lecturer)

7 7 Our Knowledge/Expertise  Pattern Recognition  Machine Learning  Digital Signal Processing  Image Processing  Embedded System  Information Security  Software Engineering

8 8 Research Focused Areas

9 9 Key Contributions – Forensic Analysis  Hand Vein Pattern Analysis Hand Vein Pattern Analysis  Speaker Identification  Acoustic Voice Feature  Image Forgery Detection Image Forgery Detection  Skin and/or Hair Analysis Skin and/or Hair Analysis

10 10 Key Contributions – Biometrics Technology  Facial Thermal pattern analysis Facial Thermal pattern analysis  Palmprint recognition  Face recognition  Iris recognition  Ear recognition Ear recognition

11 11 Key Contributions – Security Engineering  An Embedded Camera System for Vision Based Surveillance  Hidden Weapon Detection Hidden Weapon Detection  Human behaviour and brain analysis  EEG Signal Analysis  Emotion Recognition Emotion Recognition

12 12 Future Plans in ForSe Lab  To extend and build more research activities with the research areas of the lab to attract external funding.  To focus our staffs to prepare and submit major research proposals to several funding agencies, such as, AcRF, A-Star, DSTA, DSO,…etc.  To collaborate with other major organizations, such as, I2R, Singapore Police Force, MHA and also some companies in security industry, …etc.  To continue our excellent tradition of publishing our new discoveries and theories in renowned journals, conferences,…etc.

13 13 Collaborators  International  Prof. Graham Leedham, Dean of School, University of New England, Australia  Prof. M. Kamel, IEEE Fellow, University of Waterloo, Canada  Dr. Noah Caft, MD, PhD, Assistant Professor, UCAL, USA  Prof. D. Zhang, IEEE Fellow, The Hong Kong Polytechnic University, HK  Prof. Tommy Chow, City University of Hong Kong  Local  Dr. Li Haizhou, Dr Guan Cuntai and Dr. Vladimir Pervouchine, Institute of Infocomm Research (I2R)  Dr. TAY Ming Kiong Michael, Director, Physical Evidence Division, Applied Sciences Group  Ms. LIM Chin Chin, Head, Criminalistics Laboratory, Centre for Forensic Science  Dr. LOH Tsee Foong, MD, Head and Senior Consultant, KK Women’s and Children’s Hospital  Dr. James Wong (Application Architect), PCS Security Pte Ltd

14 14 Recent Publication  L. Wang, G. Leedham and Siu-Yeung Cho, "Minutiae Feature Analysis for Infrared Hand Vein Pattern Biometrics", Pattern Recognition (JCR impact factor: 3.279), 41 (3), pp. 920-929, 2008.  Lingyu Wang, Graham Leedham and Siu-Yeung Cho, “A Physiological Vein Pattern Biometric System”, HKIE Transactions, vol. 15, iss. 4, Dec. 2008 (shortlisted paper for The HKIE Outstanding Paper Award for Young Engineers/Researchers 2008).The HKIE Outstanding Paper Award for Young Engineers/Researchers 2008  L. Wang, G. Leedham and S.-Y. Cho, "Infrared imaging of hand vein patterns for biometric purposes", IET Computer Vision (JCR impact factor: 0.667), vol. 1, Iss. 3-4, pp. 113-122, Dec. 2007.  Siu-Yeung Cho, Lingyu Wang and Wen Jin Ong, “Thermal Imprint Feature Analysis for Face Recognition”, in IEEE International Symposium on Industrial Electronics 2009, July 2009, Seoul, Korea.  Haishan Zhong, Siu-Yeung (David) Cho, Vladimir Pervouchine, Graham Leedham, “Combining Novel Acoustic Features using SVM to Detect Speaker Changing Points”, BIOSIGNALS (1) 2008: 224-227.  N.B. Puhan and N. Sudha, "A novel iris database indexing method using the iris color", Proceedings of the IEEE International Conference on Industrial Electronics and Applications, Singapore, June 2008.

15 15 Thank you!

16 16 Hand-vein pattern analysis  A vein pattern refers to the vast network of blood vessels underneath the skin of a certain part of a person’s body  Images captured in an air-conditioned office environment (20-25°C and <50% humidity) FIR Image of Back of hand imaged in a normal office environment – major veins are clearly visible NIR images of the palms of two hands NIR images of the back of the hand (left) and the wrist (right)

17 17 Hand-vein pattern analysis  We have proposed a system that recognizes the human hand vein pattern images acquired by both far and near-infrared camera, which consists of five individual stages Image Acquisition Vein Pattern Segmentation Skeletonization Shape Match Decision RawImagesFinerImages Database VeinPattern Template Image Enhancement & ROI Selection Data Collection Vein Pattern Extraction

18 18 Results Skeleton and Minutiae Points of the Vein Pattern Error Rate Curves for Minutiae Recognition Using the Modified Hausdorff Distance (EER=7.5% when the threshold is set to 25) Return

19 19 Image Forgery Detection  With the advent of low-cost and high-resolution digital cameras, and sophisticated photo editing software, digital images can be easily manipulated and altered.  create forgeries, which are indistinguishable by naked eye (a) Real image; (b) Forged version; (c) Duplicated regions Return

20 20 Skin Analysis Skin marking systemData collection system Return

21 21 Principles of Thermal Facial Patterns for Biometrics  The convective heat transfer from the flow of warm arterial blood in superficial vessels is at a temperature gradient against the cooler surrounding tissue  Creating a characteristic thermal imprint on our face  This thermal pattern provides an alternative feature sets in addition to those visible features for face recognition P. Buddharaju,et. al, “Physiology-Based face Recognition in the Thermal Infrared Spectrum”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29 no.4, pp. 613-626, April 2007.

22 22 Database  Data Collection  Thermal face images can be formed by capturing the temperature profile by the NEC TH9100SL thermal camera  Grayscale images with a resolution of 320x240 are used.  Database provided by Equinox Corporation.  Frontal thermal face dataset  300 images from 30 different subjects (10 images for each subjects).

23 23 Face Segmentation (b) Edge detection with small objects removed (c) Centre portion of the image is flood filled (d) Difference Image (f) Contrast adjusted (a) Image after enhancement (e) Mask is multiplied with the image

24 24 Extracting Thermal Minutiae Points  Use morphological top-hat operation to obtain the critical edge map  Then extract the minutiae points by the cross numbering concept. (a) Thermal face region(b) Critical edge map (c) Minutiae points

25 25 Matching the Aligned TMPs  Using the same MHD measurement  Achieved 6.7% EER Return

26 26 Ear Recognition  Rationale:  Earmarks can be used as a biometric, but a computerized system for earmarks identification does not existed.  The structure of the ear does not change radically over time, especially after the first four months of birth.

27 27 Ear Recognition  Current work:  Build a ear profile database of 38 individuals (will be extended the number later)  Implement an automatic ear detection, localization and recognition system  A 11% Equal-Error-Rate is achieved. Return

28 28 Concealed Weapon Detection  Objective: to find out the feasibility of software based image processing techniques in detecting concealed weapons with infrared (IR) thermal imager without violating the privacy of the people involved. Visible image IR image Fused IR image NEC Thermo Tracer

29 29 Concealed Weapon Detection  On-going works:  Fuzzy clustering of IR images  Advanced image registration methods  Intelligent and decision based image fusion  Robust shape matching  Collaborating with EEE staffs to work with IR and MMW image sensing Return

30 30 Human behaviour and brain analysis – Emotion Recognition Potential applications: Lie detection for forensics Crime investigation Understanding Criminal Psychology Typical real-time facial expression system Return

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