May 14, 2008 الله أكبر و لله الحمد 1 Automated Identification Systems Hany Ammar Lane Dept. of Computer Science & Electrical Engineering The 2 nd International Conference on Computer and Communication Engineering (ICCCE08), KL, Malaysia A key-Note Presentation on بسم الله الرحمن الرحيم الحمد لله و الصلاة و السلام على رسول الله

April 9, 2008 الله أكبر و لله الحمد 2 Outline Automated Identification Systems The Center for Identification Technology Research (CITeR) Examples of Automated Identification Systems Projects Automated Dental Identification Systems (ADIS) Research Team Funding Agencies Overview of ADIS and the ADIS Architecture Record Pre-processing Dental Image Retrieval Matching Summary

April 9, 2008 الله أكبر و لله الحمد 3 Automated Identification Systems Automated identification of a person based on his/her physiological or behavioral characteristics Termed as “Biometrics” Identification Fingerprint Hand Geometry Signature Dental Features Iris Voice

April 9, 2008 الله أكبر و لله الحمد 4 Automated Identification Systems APPLICATIONS INCLUDE HIGH SECURITY APPLICATIONS: financial services, health care, law enforcement, Government applications, travel and immigration, and E-commerce FORENSIC IDENTIFICATION: help solve legal cases and public issues which include bank robberies, homicides, kidnapping cases, and identifying victims of mass disasters (Post Mortem identification)

April 9, 2008 الله أكبر و لله الحمد 5 Automated Identification Systems Forensic Post-Mortem (PM) Identification Methods include: - Visual - Fingerprints - DNA - Dental Dental features - Used to identify 75% of Tsunami victims in Thailand, and 20% of 9/11 victims identified in the 1 st year compared to only 0.5% identified using DNA - Resist early decay of body tissues. - Withstand severe conditions in mass disasters. - Unique (Identification can sometimes be made from one tooth).

April 9, 2008 الله أكبر و لله الحمد 6 Automated Identification Systems Example systems Automated Dental Identification System ADIS ADIS Digital Image Rep Mrs. X PM Record: - NCIC codes - Dental Radiographs Short Match List Forensic Scientist

April 9, 2008 الله أكبر و لله الحمد 7 Automated Identification Systems Example systems Automated Ear Identification System AEIS Video Sequence Ear Segmentation and Localization Image Enhancement 2-D and 3-D Feature Extraction Identification Enrollment Decision Data - base Currently being developed WVU-UM

April 9, 2008 الله أكبر و لله الحمد 8 Automated Identification Systems Biometrics Lab at WVU – Face Video data acquisition system Collected a Database of 500 Subjects

April 9, 2008 الله أكبر و لله الحمد 9 NSF Center at WVU CITeR The US National Science Foundation Center for Identification Technology Research (CITeR) Industry/University Cooperative Research Center (I/UCRC) West Virginia University is the lead institution

April 9, 2008 الله أكبر و لله الحمد 10 Outline Automated Identification Systems The Center for Identification Technology Research (CITeR) Examples of Automated Identification Systems Projects Automated Dental Identification Systems (ADIS) Research Team Funding Agencies Overview and the ADIS Architecture Record Pre-processing Dental Image Retrieval Matching Summary

April 9, 2008 الله أكبر و لله الحمد 11 ADIS Project Research Team Prof. Hany Ammar, Dr. Gamal Fahmy, Dr. Robert Howell, Dentist, Ph.D. Students: Ayman Abaza, Diaa Nassar, Eyad Haj-Said, MS Students: Mubasher, Zainab Millwallah, Usman Qureishi, Faisal Chaudhry, Mythili, and Satya Checkuri, Ali Bahoo Prof. Anil Jain, Ph.D. Student: Hong Chen Prof. Mohammad AbdelMottaleb, Ph.D. Students: Omaima Nomair, Mohammad Mahoor, Jindan,

April 9, 2008 الله أكبر و لله الحمد 12 Support $1.5M over 5 years - This research is supported in part by the U.S. National Science Foundation (Digital Government Program) under Award number EIA to West Virginia University, - The research is also supported under Award number 2001-RC-CX-K013 from the Office of Justice Programs, National Institute of Justice, U.S. Department of Justice. Points of view in this document are those of the authors and do not necessarily represent position of the U.S. Department of Justice. - The research is conducted in Collaboration with The Criminal Justice Information Services Division (CJIS) of the US Federal Bureau of Investigation

April 9, 2008 الله أكبر و لله الحمد 13 Forensic Odontologist Compares PM Records with AM records based on: - Dental Work (e.g. Fillings, Restorations...) - Inherent Dental Characteristics (Crown Morphology, Root Morphology, Spacing …) - Very Time Consuming Process Overview Dental Identification Identification of the victims of 9/ % of the 973 identified in the first year - Identification of 2,749 took around 40 months.

April 9, 2008 الله أكبر و لله الحمد 14 Source: The Bureau of Legal Dentistry (BOLD) - [2000] Overview Dental Identification is a challenging problem AM PM

April 9, 2008 الله أكبر و لله الحمد 15 Architecture Overview

April 9, 2008 الله أكبر و لله الحمد 16 ADIS Outline Overview Record Pre-processing Dental Image Retrieval Matching Conclusion & Future Work Comments & Questions

April 9, 2008 الله أكبر و لله الحمد 17 Record Pre-processing 1- Record Cropping: global segmentation of dental films from their corresponding records. The objective: to automate the process of cropping a composite digitized dental record into its constituent films Reference Record - 16 Subject Record

April 9, 2008 الله أكبر و لله الحمد 18 Dental Record Pre-Processing Cropping based on Arch- Detection Round Right Cropping based on Factor Analysis Corner-type Classification Background Extraction Post-Processing Dental Films Record Cropping Approach

April 9, 2008 الله أكبر و لله الحمد 19 Under-segmented Record Cropping Experimental Results

April 9, 2008 الله أكبر و لله الحمد 20 By calculating “  ”, “  ” found to range between , “  ” was used to identify the Under Cropped Segments. Record cropping time ranges sec. Record Cropping Experimental Results Randomly selected test sample of 100 dental records (images) from the CJIS ADIS database, the total film count in the test set is 722.

April 9, 2008 الله أكبر و لله الحمد 21 Record Pre-processing 3- Film Type Detection: dental films classification into bitewing, periapical, or panoramic. The objective: to automate the process of dental film type detection. bitewing periapical panoramic

April 9, 2008 الله أكبر و لله الحمد 22 Record Pre-processing 4- Teeth Segmentation: Teeth segmentation from dental radiographic films. The objective: to automate the process of local segmentation of each tooth. teeth isolation into a rectangular box

April 9, 2008 الله أكبر و لله الحمد 23 Record Pre-processing 5-Tooth Contour Extraction: another level of segmentation, to extract the contour of the tooth. The objective: to extract an accurate smooth representative tooth contour, - Representative smooth contour. - Time / tooth = fraction of the second).

April 9, 2008 الله أكبر و لله الحمد 24 Record Pre-processing Experimental Result Records Correct or partially correct contour extraction (%) Errors (%) Average time (s) Perfect contour (P) Perfect crown (PC) Partially correct (C) Errors (E) 10 AM PM ALL The snake-based algorithm on the same platform takes about 5 sec compared to 0.16 sec. For a test set of 20 records, involving ~340 teeth

April 9, 2008 الله أكبر و لله الحمد 25 Record Pre-processing 6-Teeth Labeling: automatic classification of teeth into incisors, canines, premolars and molars as part of creating a dental chart. The objective: - to accurately classify and label teeth, - to accommodate a missing segment. RX7 RX6 RX5 RX4 RD7 RD6 RD5 RD4 7 M 5 P

April 9, 2008 الله أكبر و لله الحمد 26 An adult has 32 permanent teeth (8 Incisors, 4 Canines, 8 Premolars and 12 Molars). Each tooth has a specific structure and position in the mouth. Dental Atlas for the left half of the upper jaw. Record Pre-processing Dental Atlas American Medical Association,

April 9, 2008 الله أكبر و لله الحمد 27 - Teeth Classification: added the film type, designed a technique based on Linear Discriminant Analysis (FisherTeeth). - Extended the validation stage for the presence of missing tooth. Teeth Labeling Approach – Eigen Teeth Record Pre-processing

April 9, 2008 الله أكبر و لله الحمد 28 Experimental Results of teeth labeling Based on the dataset used in the literature, (50 bitewing films involving about 400 teeth). MethodMolars Average Premolars Average Labeling Time Complex Signature89.6%90.95%21.3 msec Centroid Distance90.55%87.85%21.3 msec Eigen Teeth91.67%92.86%11.5 sec Record Pre-processing

April 9, 2008 الله أكبر و لله الحمد 29 ADIS Outline Overview Record Pre-processing Dental Image Retrieval Matching Conclusion & Future Work Comments & Questions

April 9, 2008 الله أكبر و لله الحمد 30 Dental Image Retrieval 4- Potential Matches Search: searching the dental database in a fast way to find a candidate list. The objective: - to accomplish a relatively short candidate list, with a high probability of having the correct match reference. This objective directly targets the scalability of ADIS system. Candidate List Digital Image Repositories

April 9, 2008 الله أكبر و لله الحمد 31 Potential Match Search Challenges Multiple Representation Of the same tooth (RX6) Reference Record Subject Record

April 9, 2008 الله أكبر و لله الحمد 32 Potential Match Search Proposed Approaches 1- Appearance- based, namely Eigen images. low computational- cost features; Limitation: need geometric and gray-scale normalization. 2- shape –based namely moment invariant and edge orientation histogram  Limitation: need accurate teeth contour.

April 9, 2008 الله أكبر و لله الحمد 33 Potential Match Search Experimental Result (Comparison between appearance and shape based) Minimum fusion, better for shape- based. The appearance- based, better for short candidate list. The edge direction histogram achieves the same performance for slightly longer candidate list.

April 9, 2008 الله أكبر و لله الحمد 34 ADIS Outline Overview Record Pre-processing Dental Image Retrieval Matching Conclusion & Future Work Comments & Questions

April 9, 2008 الله أكبر و لله الحمد 35 Image Comparison Component

April 9, 2008 الله أكبر و لله الحمد 36 Image Comparison Component Teeth Alignment: is to align each corresponding pair, in other word to find the transformation matrix that best align the reference and subject segments. The objective: is to achieve an accurate aligned segments in few seconds, so as to allow for a faster Image Comparison Component. Teeth Alignment

April 9, 2008 الله أكبر و لله الحمد 37 A Hierarchical fusion scheme: Tooth-level fusion Case-level fusion A Ranking Scheme to Sort the Match List Micro and Macro Decision-Making (The Strategy) Image Comparison Component

April 9, 2008 الله أكبر و لله الحمد 38 Results Image Comparison Component

April 9, 2008 الله أكبر و لله الحمد 39 Outline Automated Identification Systems Example Research Projects Automated Dental Identification Systems (ADIS) Research Team Funding Agencies The ADIS Architecture Record Pre-processing Dental Image Retrieval Matching Summary

April 9, 2008 الله أكبر و لله الحمد 40 Summary Automated Identification Systems are needed in many applications in the years to come They Pose many challenging problems

April 9, 2008 الله أكبر و لله الحمد 41 Summary Timeliness Performance Teeth labeling and alignment are time consuming processes Quality of radiographs are very critical for ADIS Poor quality can affect the segmentation accuracy significantly Matching efficiency can also be affected by poor quality radiograph ADIS challenges