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Found 5 papers, 0 papers with code
An Efficient Integration of Disentangled Attended Expression and Identity FeaturesFor Facial Expression Transfer andSynthesis
In this paper, we present an Attention-based Identity Preserving Generative Adversarial Network (AIP-GAN) to overcome the identity leakage problem from a source image to a generated face image, an issue that is encountered in a cross-subject facial expression transfer and synthesis process.
Facial Expression Representation Learning by Synthesizing Expression Images
Representations used for Facial Expression Recognition (FER) usually contain expression information along with identity features.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
All-In-One: Facial Expression Transfer, Editing and Recognition Using A Single Network
In this paper, we present a unified architecture known as Transfer-Editing and Recognition Generative Adversarial Network (TER-GAN) which can be used: 1. to transfer facial expressions from one identity to another identity, known as Facial Expression Transfer (FET), 2. to transform the expression of a given image to a target expression, while preserving the identity of the image, known as Facial Expression Editing (FEE), and 3. to recognize the facial expression of a face image, known as Facial Expression Recognition (FER).
Facial Expression Recognition Using Disentangled Adversarial Learning
This expression representation is disentangled from identity component by explicitly providing the identity code to the decoder part of DE-GAN.
Facial Expression Recognition
Facial Expression Recognition (FER)
+3
Hand2Face: Automatic Synthesis and Recognition of Hand Over Face Occlusions
We then propose a model for facial occlusion type recognition to differentiate between hand over face occlusions and other types of occlusions such as scarves, hair, glasses and objects.
