Indian Institute of Information Technology, Allahabad

Computer Vision and Biometrics Lab (CVBL)

Visual Recognition

Odd Semester 2021 - 2022


Course Information

Objective of the course: The field of visual recognition has become part of our lives with applications in self-driving cars, satellite monitoring, surveillance, video analytics particularly in scene understanding, crowd behaviour analysis, action recognition etc. It has eased human lives by acquiring, processing, analyzing and understanding digital images and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information. The visual recognition encapsulates image classification, localization and detection. The course on visual recognition will help students understand new tools, techniques and methods which are influencing the visual recognition field.

Outcome of the course: At the end of this course, the students will be able apply the concepts to solve some real problems in recognition. The students will be able to use computational visual recognition for problems ranging from extracting features, classifying images, to detecting and outlining objects and activities in an image or video using machine learning and deep learning concepts. The student will be also being able to invent new methods in visual recognition for various applications.



Class meets
Monday: 04.00 - 06.00 pm, Friday: 10.00 - 12.00 pm and 04.00 - 06.00 pm; Remote

Schedule - Lectures

Date Topic Optional Reading
L01: July 30: 04.00 PM - 05.00 PM Introduction Lecture
Slide, Recorded Lecture
L02: July 30: 05.00 PM - 06.00 PM Local Features: What, Why and How
Slide, Recorded Lecture
L03: August 06: 10.00 AM - 11.00 AM Corner Detection
Slide, Recorded Lecture
L04: August 06: 11.00 AM - 12.00 PM Harris Detector and Invariance Property
Slide, Recorded Lecture
L05: August 09: 04.00 PM - 05.00 PM Blob Detection: Harris-Laplacian (LoG), SIFT (DoG), Affine Invariant Detection
Slide, Recorded Lecture
L06: August 09: 05.00 PM - 06.00 PM Feature Description: SIFT and SURF
Slide, Recorded Lecture
L07: August 13: 10.00 AM - 11.00 AM Feature Description: LBP and HOG
Slide, Recorded Lecture
L08: August 27: 10.00 AM - 11.00 AM Image Categorization and Bag of Visual Words
Slide, Recorded Lecture
L09-11: August 27: 11.00 AM - 12.00 PM & 4.00 PM - 6.00 PM Classifiers for Image Categorization: KNN, Linear Classifier, SVM, Softmax
Slide, Recorded Lecture 1 Recorded Lecture 2
L12-13: August 30: 04.00 PM - 06.00 PM Neural Networks
Slide, Recorded Lecture
L14-15: September 03: 10.00 AM - 12.00 PM Convolutional Neural Networks (CNNs)
Slide, Recorded Lecture
L16-17: September 06: 04.00 PM - 06.00 PM Training Aspects of CNN: Activation Functions, Data Split, Data Preprocessing and Weight Initialization
Slide, Recorded Lecture
L18-19: September 10: 04.00 PM - 06.00 PM Training Aspects of CNN: Optimization, Learning Rate, Regularization, Dropout, Batch Normalization, Data Augmentation and Transfer Learning
Slide, Recorded Lecture
L20-21: September 24: 04.00 PM - 06.00 PM CNN Architectures - Plain Models: LeNet, AlexNet, VGG, NiN
Slide, Recorded Lecture1, Recorded Lecture2
L22-23: October 01: 04.00 PM - 06.00 PM CNN Architectures - DAG Models: GoogleNet, ResNet, DenseNet, etc.
Slide, Recorded Lecture1, Recorded Lecture2
L24-25: October 08: 10.00 AM - 12.00 PM CNN Architectures for Object Detection - R-CNN, Fast R-CNN, Faster R-CNN, YOLO, etc.
Slide, Recorded Lecture
    L26: October 23: 10.00 AM - 11.00 AM Special Lecture on Person Recognition A Biometric Approach by Dr. Satish Kumar Singh
    Lecture Slide
      L27: October 23: 11.00 PM - 12.00 PM Special Lecture on Multimodal Biometrics A Reliable Way by Dr. Satish Kumar Singh
      Lecture Slide
        L28: October 23: 03.00 PM - 04.00 PM Special Lecture on DL Architectures for Recognition by Dr. Satish Kumar Singh
        Lecture Slide, Recorded Video
          L29: October 24: 10.00 AM - 11.00 AM Special Lecture on Hand Shape Coding Multimodal Biometric by Dr. Satish Kumar Singh
          Lecture Slide, Recorded Video
            L30: October 24: 10.00 AM - 11.00 AM Special Lecture on Face Recognition under Surveillance by Dr. Satish Kumar Singh
            Lecture Slide
              L31: October 26: 06.00 PM - 07.00 PM Special Lecture on Biometric Security by Prof. Pritee Khanna (IIITDM Jabalpur)
              Recorded Video
                L32: October 26: 07.00 PM - 08.00 PM Special Lecture on DeepFakes by Dr. Kiran Raja (NTNU Norway)
                Recorded Video
                  L33: October 26: 08.00 PM - 09.00 PM Special Lecture on Face Anti-spoofing by Dr. Shiv Ram Dubey
                  Lecture Slide, Recorded Video
                    L34: October 27: 08.00 PM - 09.00 PM Special Lecture on Facial Micro-expression Recognition by Dr. Shiv Ram Dubey
                    Lecture Slide, Recorded Video

                      Schedule - Tutorials and Labs

                      Date Topic Optional Reading
                      TL01-02: July 30: 10.00 AM - 12.00 PM Introduction to Python
                      Recorded Video
                      TL03-04: August 02: 04.00 PM - 06.00 PM Introduction to Python
                      Recorded Video
                      TL05-06: August 07: 10.00 AM - 12.00 PM Introduction to Python
                      Recorded Video
                      TL07: August 13: 11.00 AM - 12.00 PM Project Discussions
                      TL08-09: August 13: 04.00 PM - 06.00 PM Project Discussions
                      TL10-11: September 03: 04.00 PM - 06.00 PM Project Work
                      TL12-13: September 10: 10.00 AM - 12.00 PM CRP Assessment 1
                      TL14-15: October 04: 04.00 PM - 06.00 PM Project Discussions
                      TL16-17: October 08: 04.00 PM - 06.00 PM Project Discussions
                      TL18-19: October 18: 04.00 PM - 06.00 PM CRP Assessment 2

                      Grading

                      • C1 (30%): 10% Written + 20% Practice
                      • C2 (30%): 10% Written + 20% Practice
                      • C3 (40%): 20% Written + 20% Practice

                      Prerequisites

                      • Computer Programming
                      • Data Structures and Algorithms
                      • Machine Learning
                      • Image and Video Processing
                      • Ability to deal with abstract mathematical concepts

                      Disclaimer

                      The content (text, image, and graphics) used in this slide are adopted from many sources for Academic purposes. Broadly, the sources have been given due credit appropriately. However, there is a chance of missing out some original primary sources. The authors of this material do not claim any copyright of such material.