I am a doctoral student at the Pattern Recognition and Information Processing Lab (PRIP) at Michigan State University, working under the supervision of Prof. Anil K. Jain.

My current research interests include the application of Computer Vision and Deep Learning for fingerprint detection and spoofing detection.


Deep Learning

Vishesh Mistry

(+1) 517-927-9098


Michigan State University, United States

PhD in Computer Science

Session: 2019-Present

Guide: Prof. Anil K. Jain

IIT Jodhpur, India

Bachelor of Technology in Computer Science and Engineering

Session: 2015-2019

CGPA: 8.99 (On the Scale of 10.00)

Atmiya Vidya Mandir, India


Session: 2014-2015

Percentage: 95.6%

Atmiya Vidya Mandir, India


Session: 2012-2013

CGPA: 10.00 (On the Scale of 10.00)

Synthesis Of Realistic Weather Specific Images

Guide: Dr. Chiranjoy Chattopadhyay | IIT Jodhpur, India | IBM Research Labs

  • Constructed a pipeline to convert images taken in summer season to images of winter season taking into consideration fine level features like clothing, etc
  • Employed scene transfer using GANs, followed by person identification and segmentation, followed by cloth changing, and finally warping back the person onto the image
  • Extended the functionalities of the pipeline by incorporating the transfer of clothes to Indian ethnic wear

Spoofing And Liveness Detection In Fingerprints

Guide: Dr. Aditya Nigam | IIT Mandi, India

  • Developed an end-to-end network for spoofing detection using Keras and PyTorch
  • Used Single Shot Multibox Detector (SSD) network for patch extraction from fingerprints which were then passed through a 7-layer multi-input convolutional network for classification
  • The final classification score for each fingerprint was the average of the classification scores of its extracted patches

Deep Transfer Learning For The Detection Of Radical Groups’ Iconography In RealWorld Images With Only One Reference Image

Guide: Dr. Juan Manuel Corchado, Dr. Javier Prieto | BISITE Research Lab, Spain

  • Created an artificial dataset from a single radical group logo using various transformations and observed how each degree of transformation affected the performance of the entire network
  • Trained several deep CNNs such as SSD, YOLO, DeepRanking, etc. to detect the presence of radical groups’ logos in real-life images. Compared their performance to that of traditional feature descriptors such as SIFT, SURF, rSIFT, AKAZE, ORB, etc

Contact Lens Detection And Classification Using A Hierarchically Tuned Convolutional Neural Network

Guide: Dr. Aditya Nigam | IIT Mandi, India

  • Used Keras to develop a hierarchically tuned two-model deep convolutional neural network for detection and classification of contact lens from iris images, outperforming the then existing state-of-the-art techniques
  • The highly generalised network gave remarkable results without using any pre-processing or iris segmentation

GHCLNet: A Generalized Hierarchically tuned Contact Lens detection Network

Avantika Singh, Vishesh Mistry, Dhananjay Yadav, Aditya Nigam

IEEE Conference on Identity, Security and Behavior Analysis (ISBA) 2018

CLResNet : A Deep Contact Lens Convolutional Neural Network Framework for Oculus Multiclass Classification

Avantika Singh, Ragina Sinha, Rahul Mishra, Vishesh Mistry, Aditya Nigam

IEEE Conference on Signal Image and Vision Technologies (SITIS) 2017


I am an avid table-tennis player and have represented my college and school at various national-level tournaments

Secured 3rd position at the Inter-IIT Sports Meet 2016

iOS App Development

I have tried my hands at developing a few apps on the iOS platform using Swift

The most recent one was developed during Microsoft Code.Fun.Do Hackathon (2018), which checked if a contact in any user’s contact list had updated any of his/her data and automatically merged the update in the user's device