Search Results for author: Behzad Hasani

Found 6 papers, 2 papers with code

BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded Gradient

1 code implementation18 Apr 2020 Behzad Hasani, Pooran Singh Negi, Mohammad H. Mahoor

We conducted comprehensive experiments on the categorical and dimensional models of affect on the challenging in-the-wild databases of AffectNet, FER2013, and Affect-in-Wild.

Facial Expression Recognition

Bounded Residual Gradient Networks (BReG-Net) for Facial Affect Computing

no code implementations5 Mar 2019 Behzad Hasani, Pooran Singh Negi, Mohammad H. Mahoor

This paper introduces Bounded Residual Gradient Networks (BReG-Net) for facial expression recognition, in which the shortcut connection between the input and the output of the ResNet module is replaced with a differentiable function with a bounded gradient.

Facial Expression Recognition

AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild

1 code implementation14 Aug 2017 Ali Mollahosseini, Behzad Hasani, Mohammad H. Mahoor

AffectNet is by far the largest database of facial expression, valence, and arousal in the wild enabling research in automated facial expression recognition in two different emotion models.

Facial Expression Recognition

Facial Affect Estimation in the Wild Using Deep Residual and Convolutional Networks

no code implementations22 May 2017 Behzad Hasani, Mohammad H. Mahoor

Automated affective computing in the wild is a challenging task in the field of computer vision.

Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks

no code implementations22 May 2017 Behzad Hasani, Mohammad H. Mahoor

Deep Neural Networks (DNNs) have shown to outperform traditional methods in various visual recognition tasks including Facial Expression Recognition (FER).

Facial Expression Recognition

Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields

no code implementations20 Mar 2017 Behzad Hasani, Mohammad H. Mahoor

Our experimental results show that cascading the deep network architecture with the CRF module considerably increases the recognition of facial expressions in videos and in particular it outperforms the state-of-the-art methods in the cross-database experiments and yields comparable results in the subject-independent experiments.

Facial Expression Recognition Object Recognition +1

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