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Posted: June 14th, 2023
Human iris recognition using Casia , ubiris, MMU and Polaris dataset.
Local Binary pattern , RLBP, PCA, LTP.
Published paper related to LBP.
Implementation of human iris system using deep learning models such as VGG16/19, inception. Mobilenet, efficient b0,b1,b3 and b021k,b121k,b321k, Nasnet architecture.
VGG16, inception, Nasnet and mobilenet had best performance.
Published paper on performance analysis of all architecture.
Implementation of ensemble model using the best architecture from previous analysis using ML classifiers such as randomforest, logistic regression, decision tree. Stack ensemble, voting, bagging methods used for ensembling.
Work pending is Gamadion pattern on dataset and input the features extracted to my ensemble mode; and check performance.
Publish paper on comparison of performance on all dataset for LBP,RLBP,LTP , PCA and Gammadion.
Publish paper on ensemble modeling .
Thesis writing (Introduction done).
Literature and methodology in process.
April 20 is my deadline.
This is my Structure of Complete Thesis:
Chapter -1: Introduction
Chapter-2: Literature Survey
Chapter-3:
Feature Engineering based approaches for Iris recognition
Chapter-4:
Deep Learning based approaches for Iris recognition
Chapter-5:
Iris recognition in uncontrolled environment: a Deep learning based approaches
Chapter-6:
Performance analysis of Deep Learning approaches for Iris recognition
Chapter-7: Comparative analysis and conclusion.
Future scope
Congratulations on your research progress so far and good luck with meeting your deadline. Your thesis structure looks well-organized, and here are some suggestions for the remaining chapters:
Chapter 2 – Literature Survey:
This chapter should include an extensive review of the existing literature on iris recognition, with a focus on feature engineering and deep learning approaches.
You can discuss the evolution of iris recognition over time and the key contributions in the field.
The literature survey can also include a critical analysis of the limitations and challenges of the existing methods.
Chapter 3 – Feature Engineering based approaches for Iris recognition:
This chapter should focus on traditional methods of feature extraction and representation for iris recognition, such as LBP, RLBP, PCA, LTP, and Gammadion pattern.
You can discuss the working principle, advantages, and limitations of each method, as well as their performance on different datasets.
This chapter can also include a comparative analysis of these methods and their combinations in terms of accuracy, efficiency, and robustness.
Chapter 4 – Deep Learning based approaches for Iris recognition:
This chapter should focus on the recent advances in deep learning-based methods for iris recognition, such as VGG16/19, Inception, Mobilenet, and EfficientNet.
You can discuss the architecture, training process, and optimization techniques of each model, as well as their performance on different datasets.
This chapter can also include a comparative analysis of these models and their combinations in terms of accuracy, efficiency, and robustness.
Chapter 5 – Iris recognition in uncontrolled environment: a Deep learning based approaches:
This chapter should focus on the challenges of iris recognition in uncontrolled environments, such as varying illumination, occlusion, and motion blur.
You can discuss the existing methods that address these challenges, including data augmentation, transfer learning, and adversarial training.
This chapter can also include a comparative analysis of these methods and their combinations in terms of accuracy, efficiency, and robustness.
Chapter 6 – Performance analysis of Deep Learning approaches
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