40 citations · 51 across the 13 of their papers we have counts for
15 papers · 1 filter
Guided Dropout
Rohit Keshari, Richa Singh, Mayank Vatsa
Dropout is often used in deep neural networks to prevent over-fitting. Conventionally, dropout training invokes \textit{random drop} of nodes from the hidden layers of a Neural Net…
Data Fine-tuning
Saheb Chhabra, Puspita Majumdar, Mayank Vatsa +1
In real-world applications, commercial off-the-shelf systems are utilized for performing automated facial analysis including face recognition, emotion recognition, and attribute pr…
Recognizing Disguised Faces in the Wild
Maneet Singh, Richa Singh, Mayank Vatsa +2
Research in face recognition has seen tremendous growth over the past couple of decades. Beginning from algorithms capable of performing recognition in constrained environments, th…
On Matching Faces with Alterations due to Plastic Surgery and Disguise
Saksham Suri, Anush Sankaran, Mayank Vatsa +1
Plastic surgery and disguise variations are two of the most challenging co-variates of face recognition. The state-of-art deep learning models are not sufficiently successful due t…
Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition
Rishabh Garg, Yashasvi Baweja, Soumyadeep Ghosh +3
Mobile biometric approaches provide the convenience of secure authentication with an omnipresent technology. However, this brings an additional challenge of recognizing biometric p…
Supervised COSMOS Autoencoder: Learning Beyond the Euclidean Loss!
Maneet Singh, Shruti Nagpal, Mayank Vatsa +2
Autoencoders are unsupervised deep learning models used for learning representations. In literature, autoencoders have shown to perform well on a variety of tasks spread across mul…