Indian Institute of Information Technology, Allahabad

Computer Vision and Biometrics Lab (CVBL)

Visual Recognition

Jan-May 2022 Semester

Previous Offerings

Visual Recognition 2021

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
Wednesday: 11.00 AM - 01.00 PM, Thursday: 07.00 - 09.00 PM, Friday: 03.00 - 05.00 PM

Course Ethics
  • Students are strictly advised to avoid the unethical practices in the course including review tests and practice components.
  • The project component will be done in team. The team will be formed by the course instructors. The project allotment will be also done by the course instructors.
  • Students are not allowed to simply claim the existing solutions available in public domain as your own work in this course.
  • If it happens that you have already done the similar projects in any other course or with any other faculty which is allotted to you, you should immediately inform us for the same as it is not allowed to have similar projects in this course which you might have already done previously.
  • It is best to try to solve problems on your own, since problem solving is an important component of the course.
  • You are not allowed to do or continue same project in any other course and with any other faculty.
  • You are allowed to discuss class material, problems, and general solution strategies with your classmates. But, when it comes to formulating or writing solutions you must work/implement by yourself.
  • You may use free and publicly available sources, such as books, journal and conference publications, and web pages, as research material for your answers. (You will not lose marks for using external sources.) It is does not mean that you claim these existing resources as your work.
  • You may not use any paid service and you must clearly and explicitly cite all outside sources and materials that you made use of.
  • Students are not allowed to post the code/report/any other material of course project in public domain or share with any one else without written permission from course instructors.
  • We consider the use of uncited external sources as portraying someone else's work as your own, and as such it is a violation of the Institute's policies on academic dishonesty.
  • Instances will be dealt with harshly and typically result in a failing course grade.


Date Topic Resources
L01: Jan 10, 2022Course Introduction
Slide, Recorded Lecture
L02: Jan 13, 2022Local Features: What, Why and How
Slide, Recorded Lecture
L03: Jan 19, 2022Corner Detection
Slide, Recorded Lecture
L04: Jan 21, 2022Harris Detector and Invariance Property
Slide, Recorded Lecture
L05: Jan 29, 2022Blob and Region Detection
Slide, Recorded Lecture
L06: Feb 02, 2022Region Descriptors
Slide, Recorded Lecture
L07: Feb 09, 2022Local Descriptors
Slide, Recorded Lecture
L08: Feb 09, 2022Image Categorization
Slide, Recorded Lecture
L09: Feb 16, 2022Image Classifiers
Slide, Recorded Lecture
L10: Feb 18, 2022Neural Networks
Slide, Recorded Lecture
L11: March 02, 2022Convolutional Neural Networks
Slide, Recorded Lecture
L12: March 04, 2022CNN Training 1
Slide, Recorded Lecture
L13: March 09, 2022CNN Training 2
Slide, Recorded Lecture
L14: March 16, 2022CNN Architectures 1
Slide, Recorded Lecture
L15: March 23, 2022CNN Architectures 2
Slide, Recorded Lecture 1, Recorded Lecture 2
L16: March 30, 2022Object Detection
Slide, Recorded Lecture
L17: April 06, 2022Adversarial Attack
Slide, Recorded Lecture
L18: April 13, 2022Generative Models
Slide, Recorded Lecture

Course Projects

Project Code Team Members Project Title
IIITA-VR22-P01 IIT2019004 Naina Kumari, IIT2019006 Asha Jyothi Donga, IIT2019017 Shruti Nanda, IIT2019023 Utkarsh Gangwar Face Recognition using Face Super-resolution
IIITA-VR22-P02 IIT2019025 Ritesh Raj, IIT2019027 Vidushi Pathak, IIT2019036 Jyotika Bhatti, IIT2019045 Amit Singh Face Sketch Recognition using Sketch-to-Face Synthesis
IIITA-VR22-P03 IIT2019077 Gade Srinivas Priyatham Reddy, IIT2019098 Abhinav, IIT2019112 Payili Vangmayi, IIT2019118 Shikhar Gupta Person Synthesis under Different Clothing Style
IIITA-VR22-P04 IIT2019119 Prakash Toppo, IIT2019121 Gurmeet Singh, IIT2019125 Aakash Bishnoi, IIT2019129 Sanyam Agarwal Person Recognition from Aerial View
IIITA-VR22-P05 IIT2019131 Priyanshu Jain, IIT2019133 Azmeera Mounika, IIT2019137 Harsh Abhijit Thete, IIT2019140 Sagar Barman Hyperspectral Image Classification
IIITA-VR22-P06 IIT2019141 Khushi Gupta, IIT2019145 Paras Agrawal, IIT2019155 Ritik Parmar, IIT2019158 Aryan Dhakad Unsupervised Image Rerieval
IIITA-VR22-P07 IIT2019160 Tejas Dutta, IIT2019161 Aadharsh Roshan Nandhakumar, IIT2019162 Vishal Burman, IIT2019164 Saksham Sood Self-supervised Image Retrieval
IIITA-VR22-P08 IIT2019166 Arun Kumar, IIT2019167 Ansh Verma, IIT2019173 Sankalp Rajendran, IIT2019177 Rohit Kumar Gupta Network Pruning for Faster Face Recognition
IIITA-VR22-P09 IIT2019179 Sharma Sahil, IIT2019180 Rajveer, IIT2019183 Devender Kumar, IIT2019184 Pratyush Pareek Person Image Synthesis in Random Poses
IIITA-VR22-P10 IIT2019185 R Shwethaa, IIT2019186 Shah Udgam Birenbhai, IIT2019189 Nidhi Kamewar, IIT2019196 Priyanshu Face Recognition under Mask
IIITA-VR22-P11 IIT2019202 Jyoti Verma, IIT2019204 Mitta Lekhana Reddy, IIT2019208 Dhanush Vasa, IIT2019219 Gitika Yadav Facial Micro-expression Recognition
IIITA-VR22-P12 IIT2019221 Divyansh Rai, IIT2019226 Mukul Mohmare, IIT2019229 Navneet Yogesh Bhole, IIT2019230 Eshan Vaid Histopathological Colon Cancer Recognition
IIITA-VR22-P13 IIT2019236 Noonsavath Sravana Samyukta, IIT2019240 Ayush Khandelwal, IEC2019019 Vishwaas Pratap Singh, IEC2019036 Harsh Ranjan Identity Recognition using Palmprint
IIITA-VR22-P14 IEC2019053 Chandan Ahire, IEC2019061 Prabhnoor Singh, IEC2019070 Priyansha Gupta, IEC2019071 Anurag Sharma Identity Recognition using Knuckleprint
IIITA-VR22-P15 IEC2019074 Ravi Agrawal, IEC2019075 Deepak Gupta, IEC2019079 Sachin Kanyal, IEC2019086 Udhav Rana, IIT2017062 Kaustubh Chetan Parmar Transformer based COVID19 Recognition from X-Ray
IIITA-VR22-P16 MIT2021046 Koppula Krishna Sai, MIT2021059 Anwesh Panda, MIT2021079 Saurav Sagar, MIT2021082 Dhote Anurag Radhesham Pose Invariant Face Recognition
IIITA-VR22-P17 RSI2022003 Suvramalya Basak Action Recognition
IIITA-VR22-P18 RSI2021003 Neeraj Baghel Image Super-resolution


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


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


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.