MMM2020 Tutorial Session

26th International Conference on Multimedia Modeling (MMM 2020)

Jan 5 (Sun) Tutorial Session Contents Summary

 

Tutorial Ⅰ : Introduction to biometrics and anti-spoofing

  1. Biometric Applications : Finger Print / Iris Recognition / Face / Vein
    • Finger Print : widely used and still valuable
      • 6 HR Baby -> F.P. Read
      • Mobile :
        – scanned only small part of F.P. -> it drops off accuracy
        – rotations
        – spoofing attack
      • Deep Learning ?
        – There is security reason why hand-crafted features are still used
      • Algorithms
        – Point-based matching algorithm
        > Minutiae points
        > enrollment + stitching
    • Iris Recognition
      • Main Problem : Glass Occlusion & Secular Reflection
      • One landmark : Daugman’s model (patent expired at 2011)
        1) Iris Segmentation
        2) Rubber-Sheet model
        3) Feature Extraction
    • Face Recognition
      • DeepFace Algorithm (Facebook 2014)
      • Almost mature
      • Accuracy on LFW DB > 99% (almost perfect)
    • Vein Recognition
      • Under the skin
      • start to focus on vein due to its anti-spoofing ability
      • Line Pattern vs. Texture Pattern
      • Wrist Vein -> Bio Watch
  2. Anti-spoofing
    • FP
      • Local textual patterns are employed as features
    • Face
      • Photo, Video, Mask
      • Anti-spoofing
        – Motion based
        – Textural difference
        – Deep Neural Network based ( Binary Classification )
        ※ Very week for unseen dataset

 

Tutorial Ⅱ : Recent advances in deep novelty detection for medical imaging

  1. Medical Imaging
    • CT, MRI, ultrasonic, …
    • Anomalies : Uncommon patterns in images
    • Anomaly Detection in Medical Imaging
      • Early Detection
      • Problems in real world
  2. Anomaly Detection System (ADS )
    • Localize anomalies and quantify their differences
    • Three Approaches
      • Supervised
        – “Chest X-ray 14” Dataset
        > kaggle dataset [Link]
        > 112,120 images
      • Unsupervised
        – Abnormal vs Normal (clustering)
        – difficult to recognize without expertise
        – require to define dissimilarity measures
      • Semi-Supervised
        – modeling normality only
  3. Challenges in Anomaly Detection
    • Modeling methods of normal cases
      • Representation learning of medical images
        – Curse of dimensionality : 3D/4D volume (CT, MRI), large 2D image (X-Ray)
      • Lack of well-defined representative boundary of normal cases
    • Data collection from healthy subjects
      • Good spread of normal cases : represent the diversity of target population
      • Factors to consider
        – Age, gender, imaging modality, hospital, and imaging protocol
    • Generalization of models
      • How to build more generalized models of normality with limited sample
      • Data augmentation, hyperparameter settings, adding noise, …
    • Anomaly scores
      • Margin from the boundaries of normal cases
      • Evaluation : Pixel-wise, patch-wise, region-wise, image-wise
  4. Models
    • Auto Encoders
      • Robust Deep Autoencoders (robust PCA)
      • Deep Autoencoding Gaussian Mixture Model
      • Adversarial Autoencoder
    • GAN
      • Deep convolutional GAN (AnoGAN)
      • GANomaly
    • One-class classification Methods
      • Deep SVDD (Support Vector Data Description)
      • OC – CNN (one-class CNN)
      • Adversarially Learned One-class classifier for Novelty Detection

 

Tutorial Ⅲ : Haptic Interaction with multimedia data

  1. Haptic Actuator, 양태헌, 고려대
    • Apple’s TAPTIC engine
      • Designed to big
    • Touch Pad
      • Taptic Engine + Force Sensor
        small horizontal actuation creates illusion of button sensation
    • Smart Fluid-based haptic
  2. Electroactive Polymer Actuator, 김상윤, 경희대
    • Kinesthetic sensation
    • Tactile sensation
    • CES 2019 Trend : Rigid -> Flexible
    • Wave-form ePVC gel => to increase vibration
  3. Haptic Rendering, 전석희, 경희대 ( 소개 링크: [Link] )
    • VR game player since 2013
    • Two purpose of haptic system
      • Reproduction of real ( in VR or Teleoperation )
      • Another information ( in HCI )
    • Three kinds of Haptics
      • Human Haptics
      • Computer Haptics
        Haptic Rendering
        Haptic Modeling
      • Machine Haptics
    • Haptic Rendering
      • Collision Detection
        – Much Faster than Computer Graphics << 1 ms
        – All computation should be done within 1 ms
    • Haptic Modeling
      • Stiffness, texture, geometric modeling, friction, inertia, weight

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