no code implementations • 1 May 2020 • Kamran Ali, Charles. E. Hughes
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.
no code implementations • 30 Nov 2019 • Kamran Ali, Charles. E. Hughes
Representations used for Facial Expression Recognition (FER) usually contain expression information along with identity features.
no code implementations • 16 Nov 2019 • Kamran Ali, Charles. E. Hughes
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).
no code implementations • 28 Sep 2019 • Kamran Ali, Charles. E. Hughes
This expression representation is disentangled from identity component by explicitly providing the identity code to the decoder part of DE-GAN.
no code implementations • 1 Aug 2017 • Behnaz Nojavanasghari, Charles. E. Hughes, Tadas Baltrusaitis, Louis-Philippe Morency
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.