BTAS 2019 (original) (raw)

Robust Tattoo Detection and Retrieval Competition (RTDRC 2019)

Organizers: Prof. Shiguang Shan, A/Prof. Hu Han, Dr Abhijit Das, Dr Antitza Dantcheva

Despite the enormous progress in biometrics-based primary modalities such as the face, iris and fingerprint, unimodal biometrics identification has not been accepted in forensics [5, 6]. Tattoos, which constitute a pertinent and highly distinctive soft biometric trait, can be particularly useful in describing wanted or missing people, or even unidentified bodies. Hence, tattoos are highly instrumental in person identification. Consequently, research on tattoo-based biometrics has gained significant interest in the last few years. In order to explore the potential of tattoos, various research directions have been proposed in the literature. However, existing tattoo search methods or tattoo retrieval techniques mainly focus on the matching of cropped tattoos. Therefore, these topics require more analysis. Open research problems include tattoo detection, as well as localization, i.e., determining whether an image contains a tattoo and if so, segmentation of the tattoo.

In addition, Sketch-Based Tattoo Search can be instrumental in many scenarios, e.g., if the surveillance image of a crime scene is not available, and the query is a tattoo sketch, drawn based a witness description. Therefore, it is important to evaluate the matching performance in similar scenarios.

We note that tattoo detection retrieval techniques can get highly affected by the change in sample quality, acquisition technique, etc. Motivated by the past competition on tattoo biometrics, namely Tatt-C and Tatt-E, and to further advance associated research, we host this competition focusing on the robustness of the evaluation pertained to tattoo detection and retrieval methodologies involving cross-dataset evaluation. The competition will focus on the following three tasks: (i) tattoo detection, (ii) tattoo retrieval, and (iii) tattoo sketch-based retrieval

An Improved Approach to Preprocessing for Fingerprint Recognition Algorithms

Demonstrators: Ashok Patel, Meghna Patel, Satyam Parikh

A demonstration of an experimental algorithm to enhance some of the critical phases of preprocessing to removing the noise, and make clear the fingerprint image for feature extraction. The algorithm also achieves enhancements in the post-processing phases for eliminating falsely extracted minutiae, to extract exact core point detection, and improving the matching of valid minutiae. The algorithm has been tested using FVC2000 and FingerDOS databases for measuring the average FMR = 1% and FNMR = 1.43% and accuracy 98.7% for both databases. There are several reasons, like displacement of finger during scanning, environmental conditions, behavior of user, etc., that cause a reduction in valid fingerprint minutiae and consequently acceptance rate during fingerprint recognition. However, the result and accuracy of fingerprint recognition depends on the presence of valid minutiae.

Spotlight Orals for Doctoral Consortium

Robust Face Recognition under Facial Alterations
Presenter: Puspita Majumdar

Multimodal Biometrics for Continuous Personalised Well-Being Monitoring
Presenter: Joao R. Pinto

Facial Recognition using Body-Worn Cameras
Presenter: Julia Bryan

Doctoral Consortium (Poster Only)

Gender Classification using Multimodal Biometrics
Presenter: Abhijit Patil

Advances in Iris Segmentation and Iris Features
Presenter: Ritesh Vyas

Tutorial Schedule:

Title: Recent Advances in Heterogeneous Face Recognition (HFR): Infrared-to-Visible Matching

Speakers: Cunjian Chen (Michigan State University), Shuowen (Sean) Hu (U.S. Army Research Laboratory, Ben Riggan (University of Nebraska-Lincoln), Nathaniel J. Short, (Booz Allen Hamilton), and Vishal M. Patel (Johns Hopkins University)

Abstract: This tutorial will address the recent advancements for the emerging research area of heterogeneous face recognition (HFR) from mainly an academic perspective but with insights from government and industry, which seeks to match facial probe imagery acquired in one modality against a gallery database of facial images acquired in another modality. HFR has strong potential to provide new capabilities for law enforcement, the military, and the intelligence community. In this tutorial, we will focus on infrared-to-visible face recognition for low- light and nighttime applications, discussing feature extraction techniques, regression methods, and classification algorithms for matching infrared imagery to gallery databases containing only visible imagery. We will also present recent advances in exploiting sensor technology such as polarimetric imaging for HFR and discuss new algorithms such as generative adversarial network-based approaches for HFR.

Title: Estimation of Soft Biometrics from Fingerprints

Speakers: Emanuela Marasco (George Mason University) and Larry Tang (George Mason University)

Abstract: Accurate gender prediction brings benefits to several applications such as content-based indexing, security, forensics and intelligence applications. A gender recognizer can be combined with the output of primary identifiers to increase the recognition accuracy in challenging scenarios (e.g., partial evidence). In crime investigations, gender classification may minimize the list of suspects. Although the development of reliable gender estimators is needed, existing approaches involving traditional ridge-based fingerprint data are only 80% accurate, and often the processes are not fully automated. Research studies have shown that females exhibit a higher ridge density compared to males, due to finer epidermal ridge details. Thus, the local texture of a fingerprint is expected to offer gender cues because it can encode the ridge density structure that varies between males and females. Local textural descriptors such as Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Binarized Statistical Image Features (BSIF) have been found to be successful for this task in both high quality and degraded fingerprint images. Differences between males and females were also captured in the frequency domain through Fourier analysis and Discrete Wavelet Transform. Recent studies have explored gender signature on a small and well-defined partition of a fingerprint corresponding to the single minutiae. This methodology is able to deal well with partial fingerprints. Furthermore, given that biometric data is gathered from different sensors, analyzing the sensitivity of the feature set to device changes becomes important. In this regard, local textural descriptors have shown robustness with respect to capture bias. Additionally, logistic regression models were applied to identify the significant features for gender estimation. These models exploit quality-based NFIQ2 features as well as local texture.
This tutorial aims at bringing awareness about the ongoing research in Gender estimation from fingerprints and the challenges faced in improving the prediction accuracy. We will review the various experiments carried out, the several features extracted from fingerprints, classification models used and their efficiency in classifying gender.

Title: Face Anti-Spoofing: Past, Present and the Future

Speakers: Yaojie Liu (Michigan State University) and Xiaoming Liu (Michigan State University)

Abstract: Face is one of the most popular biometric modalities due to its convenience of usage in access control, phone unlock and etc. Despite the high recognition accuracy, face recognition systems are not designed to distinguish between real human faces and fake ones, e.g., photograph, screen. Face spoof attacks, or presentation attacks, are the real-world attacks that use those fake faces to deceive the systems to recognize them as the real live person. Thus, to safely utilizing face recognition systems, face anti-spoofing techniques are crucial in detecting spoof attacks before performing recognition.
This tutorial provides a comprehensive review of the development of face anti-spoofing technologies. We focus on solutions for RGB sensors and include discussions of various spoof attacks (e.g. print attack, replay attack, and 3D mask attack), existing databases (e.g CASIAMFSD, OULU-NPU, HKBU MAR, and SiW), and representative works (e.g. conventional approaches and deep learning-based approaches). We also plan to discuss hardware solutions (e.g. NIR, and depth camera), generalizability to unknown attacks, several practical tips of building a face anti-spoofing system, and future research directions for face anti-spoofing.

Biases in Fingerprint Recognition Systems: Where Are We At?
Emanuela Marasco (George Mason University)
Presenter: Emanuela Marasco

CHIF: Convoluted Histogram Image Features for Detecting Silicone Mask based Face Presentation Attack
Akshay Agarwal (IIIT Delhi); Mayank Vatsa (IIIT-Delhi); Richa Singh (IIIT-Delhi)
Presenter: Mayank Vatsa

On the Generalization of Detecting Face Morphing Attacks as Anomalies: Novelty vs. Outlier Detection
Naser Damer (Fraunhofer IGD); Jonas Henry Grebe (Fraunhofer IGD); Steffen Zienert (Fraunhofer Institute for Computer Graphics Research IGD); Florian Kirchbuchner ( Fraunhofer Institute for Computer Graphics Research IGD); Arjan Kuijper (Fraunhofer Institute for Computer Graphics Research IGD)
Presenter: Naser Damer

Abstract:

Over the last half a century, biometrics provided leadership in many areas of pattern recognition and machine learning through many practical challenges while AI was going through its winter hibernation. Unaware of the challenging problems in biometrics use in large scale deployments and its grave societal impact, biometrics was considered as a solved problem by research community in mid-eighties to early nineties. In this talk, we will show how every aspect of modern AI including detection, feature extraction, matching, classification, extreme-scale classification, security, adversarial attacks, privacy, bias/fairness, ethics, explainability has been widely researched in the biometrics community much in advance before they became burning problems in AI. Many of the problems were first encountered and addressed in biometrics, e.g., scale of classification (starting with PCASYS), identification, detection (face), adoption difficulties in real world, privacy/security, datasets, standardization, enhancement, to just name a few. Successfully overcoming many operational difficulties and teething user acceptance issues, biometrics has matured into handling billions of users through mobile phones and large scale government programs. This talk will review some of the component technologies that enabled the phenomenal success of biometrics recently seen at an unprecedented level as well highlight how some of the revolutionary technologies in terms of blockchain and advanced security techniques like fully homomorphic encryption can bring biometrics even closer to masses. The rest of the machine learning and AI community will benefit as history will repeat again with the biometrics beacon showing the path for AI/ML to move forward.

Dr. Nalini K. Ratha is a Research Staff Member at IBM Thomas J. Watson Research Center, Yorktown Heights, NY. He received his B. Tech. in Electrical Engineering from Indian Institute of Technology, Kanpur, M.Tech. degree in Computer Science and Engineering also from Indian Institute of Technology, Kanpur and Ph. D. in Computer Science from Michigan State University. He has authored more than 100 research papers in the area of biometrics and has been co-chair of several leading biometrics conferences and served on the editorial boards of IEEE Trans. on PAMI, IEEE Trans. on SMC-B, IEEE Trans. on Image Processing and Pattern Recognition journal. He has co-authored a popular book on biometrics entitled “Guide to Biometrics” and also co-edited two books entitled “Automatic Fingerprint Recognition Systems” and “Advances in Biometrics: Sensors, Algorithms and Systems”. He has offered tutorials on biometrics technology at leading IEEE conferences and also teaches courses on biometrics and security. He is Fellow of IEEE, Fellow of IAPR and an ACM Distinguished Scientist. His research interests include biometrics, pattern recognition and computer vision. He has been an adjunct professor at IIIT Delhi, Cooper Union and NYU-Poly. During 2011-2012 he was the president of the IEEE Biometrics Council. At IBM, he has received several awards including a Research Division Award, Outstanding Innovation Award and Outstanding Technical Accomplishment Award along with several patent achievement awards. Recently he has been designated as an IBM Research Master Inventor.

08:00 AM

Registration Opens

In front of Ballroom D

9:00-12:30 PM

Tutorial I: Recent Advances in Heterogeneous Face Recognition (HFR): Infrared-to-Visible Matching

Speakers: Cunjian Chen (Michigan State University), Shuowen (Sean) Hu (U.S. Army Research Laboratory, Ben Riggan (University of Nebraska-Lincoln), Nathaniel J. Short, (Booz Allen Hamilton), and Vishal M. Patel (Johns Hopkins University)

Room 14

9:00-12:30 PM

Tutorial II: Estimation of Soft-biometrics from fingerprints

Speakers: Emanuela Marasco (George Mason University) and Larry Tang (George Mason University).

Room 15

10:30–11:00 AM

Coffee Break

In hallway near rooms

12:30-02:00 PM

Lunch on Your Own

2:00-5:30 PM

Tutorial III: Face Anti-Spoofing: Past, Present and the Future

Speakers: Yaojie Liu (Michigan State University) and Xiaoming Liu (Michigan State University)

Room 14

1:30–5:30 PM

Special Session on Generalizability and Adaptability in Biometrics (GAPinB)

Room 15

02:00–02:05 PM

Opening Remarks

Presenter: Kiran Raja

02:06–02:25 PM

Biases in Fingerprint Recognition Systems: Where Are We At?

Emanuela Marasco (George Mason University)

Presenter: Emanuela Marasco

02:26–02:45 PM

CHIF: Convoluted Histogram Image Features for Detecting Silicone Mask based Face Presentation Attack

Akshay Agarwal (IIIT Delhi); Mayank Vatsa (IIIT-Delhi); Richa Singh (IIIT-Delhi)

Presenter: Mayank Vatsa

02:46–03:05 PM

On the Generalization of Detecting Face Morphing Attacks as Anomalies: Novelty vs. Outlier Detection

Naser Damer (Fraunhofer IGD); Jonas Henry Grebe (Fraunhofer IGD); Steffen Zienert (Fraunhofer Institute for Computer Graphics Research IGD); Florian Kirchbuchner ( Fraunhofer Institute for Computer Graphics Research IGD); Arjan Kuijper (Fraunhofer Institute for Computer Graphics Research IGD)

Presenter: Naser Damer

03:05–03:10 PM

Closing Remarks

Presenter: Kiran Raja

3:30-3:45 PM

Coffee break

In hallway near rooms

08:00 AM

Registration Opens

In front of Ballroom D.

9:00-9:15 AM

Opening remarks

9:15-11:00 AM

Oral session I

Session chairs: Adam Czajka, Massimo Tistarelli

Ballroom D

56. A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain Knowledge Anshuman Suri (IIIT-Delhi); Mayank Vatsa (IIIT-Delhi); Richa Singh (IIIT-Delhi)

71. Attribute-Guided Coupled GAN for Cross-Resolution Face Recognition Veeru Talreja (West Virginia University); Fariborz Taherkhani (West Virginia University); Nasser Nasrabadi (West Virginia University); Matthew Valenti (West Virginia University)

87. Subclass Contrastive Loss for Injured Face Recognition Puspita Majumdar (IIIT Delhi); Saheb Chhabra (IIITD); Richa Singh (IIIT-Delhi); Mayank Vatsa (IIIT-Delhi)

**Short Orals:**20. Hybrid Dictionary Learning and Matching for Video-based Face Verification Jingxiao Zheng (University of Maryland, College Park); Jun-Cheng Chen (University of Maryland); Vishal Patel (Johns Hopkins University); Carlos Castillo (University of Maryland); Rama Chellappa (University of Maryland)

39. Effects of Postmortem Decomposition on Face Recognition David Bolme (Oak Ridge National Labs); David Cornett (Oak Ridge National Laboratory); Dawnie Steadman (University of Tennessee); Kelly Sauerwein (National Institute of Standards and Technology); Tiffany Saul (Middle Tennessee State University)

40. How to Save Your Face: a Facial Recognition Method Robust Against Image Reconstruction Marcin Plata (Wrocław University of Science and Technology); Piotr Syga (Wrocław University of Science and Technology); Marek Klonowski (Wrocław University of Science and Technology)

51. Securing CNN Model and Face Template using Blockchain Akhil Goel (Indraprastha Institute of Information Technology, Delhi); Akshay Agarwal (IIIT Delhi); Mayank Vatsa (IIIT-Delhi); Richa Singh (IIIT-Delhi); Nalini Ratha (IBM)

52. LC-DECAL: Label Consistent Deep Collaborative Learning for Face Recognition Lamha Goel (IIIT Delhi); Mayank Vatsa (IIIT-Delhi); Richa Singh (IIIT-Delhi)

54. Identity-Aware Deep Face Hallucination via Adversarial Face Verification Hadi Kazemi (WVU); Fariborz Taherkhani (West Virginia University); Nasser Nasrabadi (West Virginia University)

63. MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices Chi Nhan Duong ( Concordia University); Kha Gia Quach (Concordia University); Ibsa K Jalata (Univeristy of Arkansas); Ngan Le (Carnegie Mellon University); Khoa Luu (University of Arkansas)

67. Realistic Dreams: Cascaded Enhancement of GAN-generated Images with an Example in Face Morphing Attacks Naser Damer (Fraunhofer IGD); Fadi Boutros (Fraunhofer IGD); Alexandra Moseguí Saladié (Fraunhofer Institute for Computer Graphics Research IGD); Florian Kirchbuchner ( Fraunhofer Institute for Computer Graphics Research IGD); Arjan Kuijper (Fraunhofer Institute for Computer Graphics Research IGD and Mathematical and Applied Visual Computing group, TU Darmstadt)

11:00–11:30 AM

Coffee Break

Exhibit Hall jointly with FedID

11:30-12:00 PM

Spotlight Orals for Doctoral Consortium

Ballroom D

Robust Face Recognition under Facial Alterations
Presenter: Puspita Majumdar

Multimodal Biometrics for Continuous Personalised Well-Being Monitoring
Presenter: Joao R. Pinto

Facial Recognition using Body-Worn Cameras
Presenter: Julia Bryan

12:00-1:30 PM

Lunch on Your Own

12:00-01.30 PM

Doctorial Consortium Lunch

Room 14

01:30-3:30 PM

Oral session II

Session chairs: Emanuela Marasco, Richa Singh

Ballroom D

50. Deep Learning-Based Feature Extraction in Iris Recognition: Use Existing Models, Fine-tune or Train From Scratch? Aidan Boyd (University of Notre Dame)*; Adam Czajka (University of Notre Dame); Kevin Bowyer (University of Notre Dame)

88. Defending Against Adversarial Iris Examples Using Wavelet Decomposition Sobhan Soleymani (West Virginia University)*; Ali Dabouei (West Virginia university); Jeremy Dawson (West Virginia University); Nasser Nasrabadi (West Virginia University)

28. Rotation Invariant Finger Vein Recognition Bernhard Prommegger (University of Salzburg)*; Andreas Uhl (University of Salzburg)

49. Cross-sensor iris recognition using adversarial strategy and sensor-specific information Jianze Wei (Institute of Automation，Chinese Academy of Sciences); Yunlong Wang (Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation, Chinese Academy of Sciences (CASIA) ); Xiang Wu ( Institute of Automation, Chinese Academy of Sciences); Zhaofeng He (Beijing IrisKing Co., Ltd); Ran He (Institute of Automation, Chinese Academy of Sciences); Zhenan Sun (Chinese of Academy of Sciences)*

**Short Orals:**70. Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos Huy Nguyen (SOKENDAI)*; Fuming Fang (National Institute of Informatics); Junichi Yamagishi (National Institute of Informatics); Isao Echizen (National Institute of Informatics)

72. Zero-Shot Deep Hashing and Neural Network Based Error Correction for Face Template Protection Veeru Talreja (West Virginia University)*; Matthew Valenti (West Virginia University); Nasser Nasrabadi (West Virginia University)

99. On Learning Joint Multi-biometric Representations by Deep Fusion Naser Damer (Fraunhofer IGD)*; Kristiyan Dimitrov (Fraunhofer IGD); Andreas Braun (Fraunhofer IGD); Arjan Kuijper (Fraunhofer Institute for Computer Graphics Research IGD and Mathematical and Applied Visual Computing group, TU Darmstadt)

103. IARPA Janus Benchmark Multi-Domain Face Nathan D Kalka (Noblis)*; James A Duncan (Noblis); Jeremy Dawson (West Virginia University); Charles Otto (Noblis)

7. Cosmetic-Aware Makeup Cleanser Yi Li (Institute of Automation, Chinese Academy of Sciences)*; Huaibo Huang (University of Chinese Academy of Sciences); Junchi Yu (Institute of Automation, Chinese Academy of Sciences); Ran He (Institute of Automation, Chinese Academy of Sciences); Tieniu Tan (NLPR, China)

26. Gaze-angle Impact on Iris Segmentation using CNNs Ehsaneddin Jalilian (University of Salzburg)*; Andreas Uhl (University of Salzburg), Mahmut Karakaya (University of Central Arkansas)

45. Perception of Image Features in Post-Mortem Iris Recognition: Humans vs Machines Adam Czajka (University of Notre Dame); Mateusz M Trokielewicz (Warsaw University of Technology)*; Piotr Maciejewicz (Medical University of Warsaw)

61. ThirdEye: Triplet Based Iris Recognition without Normalization Sohaib Ahmad (University of Connecticut)*; Benjamin Fuller (University of Connecticut)

69. Robust Subject-invariant Feature Learning for Ocular Biometrics in Visible Spectrum Sai Narsi Reddy Donthi Reddy (University of Missouri - Kansas City)*; Ajita Rattani (University of Missouri - Kansas City); Prof. Reza Derakhshani (University of Missouri-Kansas City)

03:30-04:00 PM

Coffee Break

Exhibit Hall

03:45-6:30 PM

Poster session 1+ Reception Jointly with FedID starting at 4:45

Session chairs: David Bolme, Jeremy Dawson, Sean Hu

Exhibit Hall

Doctoral Consortium (Poster Only)
Gender Classification using Multimodal Biometrics
Presenter: Abhijit Patil

Advances in Iris Segmentation and Iris Features
Presenter: Ritesh Vyas

Posters will be given by all papers with long and short orals on Tuesday. 23 + 5 Doctoral Consortium Posters = 28 total boards

Demonstration: An Improved Approach to Preprocessing for Fingerprint Recognition Algorithms
Demonstrators: Ashok Patel, Meghna Patel, Satyam Parikh

08:30 AM

Registration Opens

In front of Ballroom

9:00-11:00 AM

Oral session III

Session chairs: Kiran Raja, Nasser Nasrabadi

Ballroom D

19. The Effect of Broad and Specific Demographic Homogeneity on the Imposter Distributions and False Match Rates in Face Recognition Algorithm Performance John J. Howard (The Maryland Test Facility)*; Yevgeniy Sirotin (The Maryland Test Facility); Arun Vermury (DHS S&T)

24. Reliable Age and Gender Estimation from Face Images: Stating the Confidence of Model Predictions Philipp Terhörst (Fraunhofer Institute for Computer Graphics Research IGD)*; Marco Huber (TU Darmstadt); Jan Kolf (TU Darmstadt); Ines Zelch (TU Darmstadt); Naser Damer (Fraunhofer IGD); Florian Kirchbuchner ( Fraunhofer Institute for Computer Graphics Research IGD); Arjan Kuijper (Fraunhofer Institute for Computer Graphics Research IGD and Mathematical and Applied Visual Computing group, TU Darmstadt)

29. User profiling using sequential mining over web elements Matan Levi (IBM)*; Itay Hazan (IBM)

9. A Genetic Algorithm Enabled Similarity-Based Attack on Cancellable Biometrics Xingbo Dong (Monash university,Malaysia)*; Zhe Jin (Monash University Malaysia); Andrew Beng Jin Teoh (Yonsei University)

**Short Orals:**17. An End-to-End Convolutional Neural Network for ECG-Based Biometric Authentication João R Pinto (INESC TEC)*; Jaime Cardoso (Portugal)

84. MasterPrint Attack Resistance: A Maximum Cover based Approach for Automatic Fingerprint Template Selection Aditi Roy (NYU Tandon School of Engineering)*; Nasir Memon (New York University, USA); Arun Ross (Michigan State University)

93. A Locality Sensitive Hashing Based Approach for Generating Cancelable Fingerprints Templates Debanjan Sadhya (ABV-IIITM Gwalior)*; Zahid Akhtar (University of Memphis); Dipankar Dasgupta (University of Memphis)

5. Make the Bag Disappear: Carrying Status-invariant Gait-based Human Age Estimation using Parallel Generative Adversarial Networks Xiang Li (Nanjing University of Science and Technology)*; Yasushi Makihara ("""Osaka University, Japan"""); Chi Xu (Nanjing University of Science and Technology); Prof. Yasushi Yagi (Osaka University); Mingwu Ren (Nanjing University of Science and Technology)

85. Hierarchical Bloom Filter Framework for Security, Space-efficiency, and Rapid Query Handling in Biometric Systems Sumaiya Shomaji (University of Florida)*; Fatemah Ganji (University of Florida); Damon L Woodard (University of Florida, USA); Domenic Forte (University of Florida)

38. Palmprint Recognition Using Realistic Animation Aided Data Augmentation Pranjal Swarup (Nanyang Technological University)*; Wai-Kin Adams Kong (Nanyang Technological University)

13. Unconstrained Thermal Hand Segmentation Ewelina Bartuzi (Warsaw University of Technology)*; Mateusz M Trokielewicz (Warsaw University of Technology)

79. Smartphone Camera De-identification while Preserving Biometric Utility Sudipta Banerjee (Michigan State University)*; Arun Ross (Michigan State University)

81. Facial Attribute Classification: A Comprehensive Study and a Novel Mid-Level Fusion Classifier Abdulaziz A Alorf (Virginia Tech)*; A Lynn Abbott (Virginia Tech); Kadir Peker (Independent Consultant)

11:00–11:30 AM

Coffee Break

Exhibit Hall jointly with FedID

12:00-1:00 PM

Lunch on Your Own

01:00–02:00 PM

Panel: Misconceptions, Realities, and Future of Identity: Government, Academic, and Industrial Perspectives

Logan O'Shaughnessy, Attorney, Privacy and Civil Liberties Oversight Board

Moderator: Stephanie Schuckers

https://events.afcea.org/FedID19/Public/SessionDetails.aspx?FromPage=Sessions.aspx&SessionID=7354 &SessionDateID=548

Jointly with FedID

Exhibit Hall Theater

02:00–02:30 PM

Coffee Break

Exhibit Hall

02:30–03:30 PM

Remaining Challenges in Automated Face Recognition

Keynote Speaker: Brendan Klare, Rank One Computing

Ballroom D

03:30–04:30 PM

Entrepreneurship Panel

Moderator: Gloria G. See

https://entrepreneurship.ieee.org/session/6400/

Ballroom D

04:30–05:30 PM

Poster session 2

Session chairs: John Howard, Asem Othman

Ballroom D

Posters will be given by all papers with long and short orals on Wednesday and Thursday. 16 total

Demonstration: An Improved Approach to Preprocessing for Fingerprint Recognition Algorithms
Demonstrators: Ashok Patel, Meghna Patel, Satyam Parikh

05:30–08:30 PM

Cocktail reception, Banquet and Best Paper Awards

BTAS Attendees Only

Room 14-15

08:30 AM

Registration Opens

In front of Ballroom

09:45–10:15 AM

Coffee Break

Exhibit Hall

10:15-11:15 AM

Special Talk by IEEE Biometrics Council Leadership Award Winner

Session chair: Ioannis Kakadiaris
Title: Rejoicing Biometrics: The Unsung Pioneer and Crystal Ball for AI

Presenter: Nalini Ratha

Ballroom D

11:15-12:15 PM

Best Reviewed Paper Orals

Session chairs: Andreas Uhl, Stephen Elliot

Ballroom D

105. Morton Filters for Iris Template Protection - An Incremental and Superior Approach Over Bloom Filters Kiran Raja (NTNU)*; Raghavendra Ramachandra (NTNU, Norway); Christoph Busch (Norwegian University of Science and Technology)

43. Finger-Vein Template Protection based on Alignment-Free Hashing Simon Kirchgasser (University of Salzburg); Zhe Jin (Monash University Malaysia); Yenlung Lai ( Monash University Malaysia); Andreas Uhl (University of Salzburg)*

80. Face Phylogeny Tree: Deducing Relationships Between Near Duplicate Face Images Using Legendre Polynomials and Radial Basis Functions Sudipta Banerjee (Michigan State University)*; Arun Ross (Michigan State University)

12:15-12:30 PM

Closing Remarks

Ballroom D

A. Advice for Oral Presenters

Please lookup at the program to find out which day is your presentation scheduled. Your oral presentation should last: 15 mins if you presenting in sessions 1-3 and 18 mins if you are presenting in the “Best Reviewed Papers” Session. The audience will have 2 mins to ask you questions. In addition, you are kindly requested to prepare a poster to present at the poster session that day. Please find below some advice:

Oral Presentation
  1. Your session chair will communicate with you to ask
    • Who is the presenter
    • Learn one thing about the presenter to use for the introduction
  2. Please think about how to make your talk engaging and elucidating.
  3. Give the audience something they can walk away with: valuable insights, actionable information, and fresh perspectives.
  4. Make sure that your review of the related work points out clearly what are the gaps in the literature and how your paper fits in the literature (meaning which gap of these gaps does it eliminate)
  5. Make sure that you clearly state what the contributions of this paper are and what are the advantages of your solution.
  6. Make sure you state what is the impact of your work (basically who cares).
  7. State the limitations of your work and offer a discussion and a reflection on the results of your method/system.
  8. Make sure that you state why you compare with specific algorithms and why did you select the databases that you provide results.
  9. Mention if your improvement is statistically significant or not.
  10. If you processed data under an IRB protocol add the following sentences: “The data were acquired using the IRB protocol titled “” approved by or “The data were processed using the IRB protocol titled “” approved by .
  11. Practice, Practice, Practice!
  12. Finish on time!
Poster Presentation
  1. Prepare a 30 s presentation that presents the overview of your work to the attendee that comes to your poster
  2. Prepare a 3 min presentation to share with those that would like to know the details
  3. The poster boards will be 8 ft wide and 4 ft tall.

B. Advice for Poster Presenters

Please lookup at the program to find out which day is your presentation scheduled. Along with your poster presentation, you are kindly asked to offer a 5 min presentation to entice the audience to come to your poster. Your oral presentation should last for 5 mins. The audience will have 1 min to ask you questions. Please find below some advice:

Oral Presentation
  1. Your session chair will communicate with you to ask
    • Who is the presenter
    • Learn one thing about the presenter to use for the introduction
  2. Please think about how to make your talk engaging and elucidating. You are to provide the reason an attendee should come to your poster.
  3. Practice, Practice, Practice!
  4. Finish on time!
Poster Presentation
  1. Give the audience something they can walk away with: valuable insights, actionable information, and fresh perspectives.
  2. Make sure that your review of the related work points out clearly what are the gaps in the literature and how your paper fits in the literature (meaning which gap of these gaps does it eliminate)
  3. Make sure that you clearly state what the contributions of this paper are and what are the advantages of your solution.
  4. Make sure you state what the impact of your work (basically who cares) is
  5. State the limitations of your work and offer a discussion and a reflection on the results of your method/system.
  6. Make sure that you state why you compare with specific algorithms and why did you select the databases that you provide results.
  7. Mention if your improvement is statistically significant or not.
  8. If you processed data under an IRB protocol add the following sentences: “The data were acquired using the IRB protocol titled “” approved by or “The data were processed using the IRB protocol titled “” approved by .
  9. Prepare a 30 s presentation that presents the overview of your work to the attendee that comes to your poster
  10. Prepare a 3 min presentation to share with those that would like to know the details
  11. The poster boards will be 8 ft wide and 4 ft tall.

Best Posters:

Defending Against Adversarial Iris Examples Using Wavelet Decomposition, Sobhan Soleymani; Ali Dabouei; Jeremy Dawson; Nasser Nasrabadi, West Virginia University
Face Phylogeny Tree: Deducing Relationships Between Near-Duplicate Face Images Using Legendre Polynomials and Radial Basis Functions, Sudipta Banerjee and Arun Ross, Michigan State University

Best Student Paper:

Subclass Contrastive Loss for Injured Face Recognition, Puspita Majumdar, Saheb Chhabra, Richa Singh, Mayank Vatsa, IIIT-Delhi, India

Best Paper:

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