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Indu Joshi
Researcher at Indian Institute of Technology Delhi
Publications - 9
Citations - 76
Indu Joshi is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topics: Fingerprint (computing) & Fingerprint recognition. The author has an hindex of 2, co-authored 9 publications receiving 32 citations. Previous affiliations of Indu Joshi include National Institute of Technology Delhi.
Papers
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Proceedings ArticleDOI
Latent Fingerprint Enhancement Using Generative Adversarial Networks
TL;DR: A Generative Adversarial Network based latent fingerprint enhancement algorithm is proposed to enhance the poor quality ridges and predict the ridge information and helps the standard feature extraction and matching algorithms to boost latent fingerprints matching performance.
Proceedings ArticleDOI
Explainable Fingerprint ROI Segmentation Using Monte Carlo Dropout
Indu Joshi,Riya Kothari,Ayush Utkarsh,Vinod K Kurmi,Antitza Dantcheva,Sumantra Dutta Roy,Prem Kalra +6 more
TL;DR: Zhang et al. as mentioned in this paper proposed an explainable finger-print ROI segmentation model which indicates the pixels on which the model is uncertain, and demonstrated the effectiveness of model uncertainty as an attention mechanism to improve the segmentation performance.
Proceedings ArticleDOI
Sensor-invariant Fingerprint ROI Segmentation Using Recurrent Adversarial Learning
Indu Joshi,Ayush Utkarsh,Riya Kothari,Vinod K Kurmi,Antitza Dantcheva,Sumantra Dutta Roy,Prem Kalra +6 more
TL;DR: In this paper, a recurrent adversarial learning based feature alignment network is proposed to align the features of fingerprint images derived from the unseen sensor such that they are similar to the ones obtained from the fingerprints whose ground truth roi masks are available for training.
Book ChapterDOI
On Training Generative Adversarial Network for Enhancement of Latent Fingerprints
TL;DR: The proposed latent fingerprint enhancement model preserves ridge structure including minutiae, and discusses the role of training data i.e. various noise models which should be considered for modeling a latent fingerprint, during training a GAN.
Proceedings ArticleDOI
Data Uncertainty Guided Noise-aware Preprocessing Of Fingerprints
Indu Joshi,Ayush Utkarsh,Riya Kothari,Vinod K Kurmi,Antitza Dantcheva,Sumantra Dutta Roy,Prem Kalra +6 more
TL;DR: In this article, the authors proposed a data uncertainty-based framework which enables the state-of-the-art fingerprint pre-processing models to quantify noise present in the input image and identify fingerprint regions with background noise and poor ridge clarity.