Face Trees for Expression Recognition

Kolahdouzi, Mojtaba; Sepas-Moghaddam, Alireza; Etemad, Ali

doi:10.1109/fg52635.2021.9666986

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…One major challenge in the real-world application would be deciding whether the FSR is necessary. The soft attention mechanism is not applicable; therefore, we used a hard-selection method [94]. To this end, we use the HopeNet feature to determine whether the input should be fed to FSR subnetwork first or not.…”

Section: ) Model Selectionmentioning

confidence: 99%

Joint Super-Resolution and Head Pose Estimation for Extreme Low-Resolution Faces

et al. 2023

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State-of-the-art deep learning-based Head Pose Estimation (HPE) techniques have reached spectacular performance on High-Resolution (HR) face images. However, they still fail to achieve expected performance on low-resolution images at large scales. This work presents an end-to-end HPE framework assisted by a Face Super-Resolution (FSR) algorithm. The proposed FSR model is specifically guided to enhance the HPE performance rather than considering FSR as an independent task. To this end, we utilized a Multi-Stage Generative Adversarial Network (MSGAN) which benefit from a pose-aware adversarial loss and head pose estimation feedback to generate super-resolved images that are properly aligned for HPE. Also, we propose a degradation strategy rather than simple down-sampling approach to mimic the diverse properties of real-world Low-Resolution (LR) images. We evaluate the performance of our proposed method on both synthetic and real-world LR datasets and show the superiority of our approach in both visual and HPE metrics on the AFLW2000, BIWI, and WiderFace Datasets.INDEX TERMS Head pose estimation (HPE), face super-resolution (FSR), multi-stage generative adversarial networks (MSGAN), low-resolution (LR) face images.

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Section: ) Model Selectionmentioning

confidence: 99%

Joint Super-Resolution and Head Pose Estimation for Extreme Low-Resolution Faces

et al. 2023

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“…This paper is an extension of our prior work [29] which has been published in FG 2021 as a short paper. In comparison, this paper adds the followings: (i) We use three additional publicly available datasets, namely RAF-DB, ExpW, and CK+, to evaluate the proposed method.…”

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confidence: 95%

FaceTopoNet: Facial Expression Recognition Using Face Topology Learning

Kolahdouzi

Sepas-Moghaddam

Etemad³

2023

IEEE Trans. Artif. Intell.

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Prior work has shown that the order in which different components of the face are learned using a sequential learner can play an important role in the performance of facial expression recognition systems. We propose FaceTopoNet, an endto-end deep model for facial expression recognition, which is capable of learning an effective tree topology of the face. Our model then traverses the learned tree to generate a sequence, which is then used to form an embedding to feed a sequential learner. The devised model adopts one stream for learning structure and one stream for learning texture. The structure stream focuses on the positions of the facial landmarks, while the main focus of the texture stream is on the patches around the landmarks to learn textural information. We then fuse the outputs of the two streams by utilizing an effective attention-based fusion strategy. We perform extensive experiments on four largescale in-the-wild facial expression datasets -namely AffectNet, FER2013, ExpW, and RAF-DB -and one lab-controlled dataset (CK+) to evaluate our approach. FaceTopoNet achieves state-ofthe-art performance on three of the five datasets and obtains competitive results on the other two datasets. We also perform rigorous ablation and sensitivity experiments to evaluate the impact of different components and parameters in our model. Lastly, we perform robustness experiments and demonstrate that FaceTopoNet is more robust against occlusions in comparison to other leading methods in the area.Impact Statement-Systems that perform facial expression recognition (FER) have a wide variety of applications in realworld problems such as human-machine interaction, robotics, mental health management systems, to name but a few. Our proposed solution achieves better recognition rates in comparison to many other methods in the field of FER, allowing for development of more accurate systems. Moreover, our proposed method demonstrates more robustness against occlusions, which can be beneficial for in-the-wild scenarios. Lastly, our model can learn trees that are optimized, yet also generalize well across different datasets and perform considerably better than humandesigned face trees.

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Analysis of Semi-Supervised Methods for Facial Expression Recognition

Etemad

2022

2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII)

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Face Trees for Expression Recognition

Cited by 5 publications

References 27 publications

Joint Super-Resolution and Head Pose Estimation for Extreme Low-Resolution Faces

Joint Super-Resolution and Head Pose Estimation for Extreme Low-Resolution Faces

FaceTopoNet: Facial Expression Recognition Using Face Topology Learning

Analysis of Semi-Supervised Methods for Facial Expression Recognition

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