Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition
In this paper, we propose a simple yet effective approach, named Point Adversarial Self Mining (PASM), to improve the recognition accuracy in facial expression recognition. Unlike previous works focusing on designing specific architectures or loss functions to solve this problem, PASM boosts the network capability by simulating human learning processes: providing updated learning materials and guidance from more capable teachers. Specifically, to generate new learning materials, PASM leverages a point adversarial attack method and a trained teacher network to locate the most informative position related to the target task, generating harder learning samples to refine the network. The searched position is highly adaptive since it considers both the statistical information of each sample and the teacher network capability. Other than being provided new learning materials, the student network also receives guidance from the teacher network. After the student network finishes training, the student network changes its role and acts as a teacher, generating new learning materials and providing stronger guidance to train a better student network. The adaptive learning materials generation and teacher/student update can be conducted more than one time, improving the network capability iteratively. Extensive experimental results validate the efficacy of our method over the existing state of the arts for facial expression recognition.
Code (0)
등록된 구현이 없습니다.
Tasks
Adversarial AttackData AugmentationFacial Expression RecognitionFacial Expression Recognition (FER)Knowledge DistillationPositionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Facial Expression Translation using Landmark Guided GANs
We propose a simple yet powerful Landmark guided Generative Adversarial Network (LandmarkGAN) for the facial expression-to-expression translation using a single image, which is an important and challenging task in comput…
Facial Expression TranslationGenerative Adversarial NetworkTranslationGenerating Unrestricted 3D Adversarial Point Clouds
Utilizing 3D point cloud data has become an urgent need for the deployment of artificial intelligence in many areas like facial recognition and self-driving. However, deep learning for 3D point clouds is still vulnerable…
Adversarial AttackGenerative Adversarial NetworkUnderstanding Beauty via Deep Facial Features
The concept of beauty has been debated by philosophers and psychologists for centuries, but most definitions are subjective and metaphysical, and deficit in accuracy, generality, and scalability. In this paper, we presen…
Generative Adversarial Networkepsilon-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression Recognition
Point clouds and meshes are widely used 3D data structures for many computer vision applications. While the meshes represent the surfaces of an object, point cloud represents sampled points from the surface which is also…
Adversarial AttackFacial Expression RecognitionRobust classificationSimFLE: Simple Facial Landmark Encoding for Self-Supervised Facial Expression Recognition in the Wild
One of the key issues in facial expression recognition in the wild (FER-W) is that curating large-scale labeled facial images is challenging due to the inherent complexity and ambiguity of facial images. Therefore, in th…
Face AlignmentFacial Expression RecognitionFacial Expression Recognition (FER)