IRIS based human identification: Analogizing and exploiting PSNR and MSE techniques using MATLAB

Pandey, Madhulika; Bhatia, Madhulika; Bansal, Abhay

doi:10.1109/iciccs.2016.7542309

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…Therefore, it can prove to be an excellent feature to be used for distinguishing identical twins in the process of biometric identification and digital passport management. Hence, this drawback can be addressed using detection of iris by integrating feature extraction and Principal Component Analysis (PCA) or by using the watermarkingbased approach [32][33][34][35].…”

Section: Future Scopementioning

confidence: 99%

A Proposed Framework for Identity Verification in Passport Management Using Model Scaling and Semantic Similarity

Khurana,

Bhatia,

Battan

et al. 2023

ISI

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The dependence on images for face detection and identity verification was at odds with broad assessment which showed that coordinating with sets of non-similar images was profoundly inclined to make mistakes. The current passport management lacks security of data and requires more manpower and time, manual calculations, and verification of documents as well as the photographs. Additionally, the computerized passport examination requires accurate processing in order to carry out crucial tasks like identifying passport forgeries, looking for wanted criminals, or finding those who are ineligible for immigration, among other things because the utilization of fake passports addresses a huge danger to the security of the nation. Therefore, the purpose of this paper is to enhance the identity verification process in passport management. The paper comprises implementation of various technologies and technology driven models for serving its purpose by using the latest and efficient techniques that includes neural networks, face detection, image similarity, ROI (region of interest) and model scaling. The dataset utilized in the paper was self-prepared by the authors and consisted of 5 images each for 10 different individuals. It had been trained from scratch on different models based on CNN architecture. Out of all the three models used for training, the model that resulted in highest accuracy was further considered for research. EfficientNetB0 resulted in 82% accuracy being the highest out of all the three models. The threshold for the model was thereby calculated using z-score which compared the similarity scores and classified the images as similar or non-similar. Therefore, it can be concluded that the techniques utilized in the paper are efficient enough to determine the similarity of an image with its corresponding image.

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Section: Future Scopementioning

confidence: 99%

A Proposed Framework for Identity Verification in Passport Management Using Model Scaling and Semantic Similarity

Khurana,

Bhatia,

Battan

et al. 2023

ISI

View full text Add to dashboard Cite

show abstract

“…Images f(x,y) where x and y are the image coordinates are processed on the basis of nature and type of the problem. Digital image processing has wide applications in numerous areas like medical imaging [34][35][36][37], pattern recognition, biotechnology, computer vision and many others.…”

Section: Related Workmentioning

confidence: 99%

SVM Prediction Model Interface for Plant Contaminates

Aggarwal¹,

Bhatia²,

Madaan³

et al. 2021

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In today's time, our nature is fighting against many life-threatening problems which can even threaten the existence of life on the Earth. Pollution is one of the deadliest problems among them. It is caused primarily by means of air, water and land but air pollution is the most severe and dreadful among them. It is caused by introduction of toxic substances like oxides of Sulphur, nitrogen and carbon into the atmosphere making it unfit for living beings. Along with humans, plants have also become a victim to it, and this fact is mostly ignored. A model has been designed to predict the effect of pollution on plants. Image samples of 5 Indian oxygen rich plants namely Ocimum Tenuiflorum, Sansevieria Trifasciata, Chlorophytum Comosum, and Azadirachta Indica have been taken for analysis and various properties like shape, color, corners and texture of the plants were considered from these input RGB images. As a consequence of these properties and the pollution index value, certain calculations have been performed and the results are compared with the threshold values. Based on the range in which the calculated results lie, the plants will be categorized into a category which depicts the severity level of pollution in the environment. After applying the model on the images, a dataset was prepared and SVM classification model has been trained on it which predict with an accuracy of 85%. It has been presented in the form of an interactive user interface to predict the effect of pollution on plants. Plants are an integral part of nature and should not be ignored.

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A New Method to Classify Leaves Using Data Visualization: Spreading Awareness About Global Warming

Aggarwal

Bhatia

Pandey

2019

Proceedings of ICETIT 2019

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IRIS based human identification: Analogizing and exploiting PSNR and MSE techniques using MATLAB

Cited by 7 publications

References 5 publications

A Proposed Framework for Identity Verification in Passport Management Using Model Scaling and Semantic Similarity

A Proposed Framework for Identity Verification in Passport Management Using Model Scaling and Semantic Similarity

SVM Prediction Model Interface for Plant Contaminates

A New Method to Classify Leaves Using Data Visualization: Spreading Awareness About Global Warming

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