most citedClass adaptive threshold and negative class guided noisy annotation robust Facial Expression Recognition

3 citations · 7 across the 5 of their papers we have counts for

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cs.CV20233 cited

Class adaptive threshold and negative class guided noisy annotation robust Facial Expression Recognition

Darshan Gera, Badveeti Naveen Siva Kumar, Bobbili Veerendra Raj Kumar +1

The hindering problem in facial expression recognition (FER) is the presence of inaccurate annotations referred to as noisy annotations in the datasets. These noisy annotations are…

cs.CV2023

Masked Student Dataset of Expressions

Sridhar Sola, Darshan Gera

Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face. However, real-world face occlusion is prevalent, most…

cs.CV20232 cited

ABAW : Facial Expression Recognition in the wild

Darshan Gera, Badveeti Naveen Siva Kumar, Bobbili Veerendra Raj Kumar +1

The fifth Affective Behavior Analysis in-the-wild (ABAW) competition has multiple challenges such as Valence-Arousal Estimation Challenge, Expression Classification Challenge, Acti…

cs.CV20221 cited

Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition

Darshan Gera, Naveen Siva Kumar Badveeti, Bobbili Veerendra Raj Kumar +1

The real-world facial expression recognition (FER) datasets suffer from noisy annotations due to crowd-sourcing, ambiguity in expressions, the subjectivity of annotators and inter-…

cs.CV20221 cited

SS-MFAR : Semi-supervised Multi-task Facial Affect Recognition

Darshan Gera, Badveeti Naveen Siva Kumar, Bobbili Veerendra Raj Kumar +1

Automatic affect recognition has applications in many areas such as education, gaming, software development, automotives, medical care, etc. but it is non trivial task to achieve a…