SASE: RGB-Depth Database for Human Head Pose Estimation

Lüsi, Iiris; Escarela, Sergio; Anbarjafari, Gholamreza

doi:10.1007/978-3-319-49409-8_26

Cited by 21 publications

(9 citation statements)

References 33 publications

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“…The SASE dataset [37] has captured different head poses from 50 subjects (32 males and 18 females) via the Kinect 2. Altogether the dataset consists of around 30k images with 600+ frames per subject.…”

Section: ) Sasementioning

confidence: 99%

Learning 3D Head Pose From Synthetic Data: A Semi-Supervised Approach

et al. 2021

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Accurate head pose estimation from 2D image data is an essential component of applications such as driver monitoring systems, virtual reality technology, and human-computer interaction. It enables a better determination of user engagement and attentiveness. The most accurate head pose estimators are based on Deep Neural Networks that are trained with the supervised approach and rely primarily on the accuracy of training data. The acquisition of real head pose data with a wide variation of yaw, pitch and roll is a challenging task. Publicly available head pose datasets have limitations with respect to size, resolution, annotation accuracy and diversity. In this work, a methodology is proposed to generate pixel-perfect synthetic 2D headshot images rendered from high-quality 3D synthetic facial models with accurate head pose annotations. A diverse range of variations in age, race, and gender are also provided. The resulting dataset includes more than 300k pairs of RGB images with corresponding head pose annotations. A wide range of variations in pose, illumination and background are included. The dataset is evaluated by training a state-of-the-art head pose estimation model and testing against the popular evaluation-dataset Biwi. The results show that training with purely synthetic data generated using the proposed methodology achieves close to state-of-the-art results on head pose estimation which are originally trained on real human facial datasets. As there is a domain gap between the synthetic images and real-world images in the feature space, initial experimental results fall short of the current state-of-the-art. To reduce the domain gap, a semisupervised visual domain adaptation approach is proposed, which simultaneously trains with the labelled synthetic data and the unlabeled real data. When domain adaptation is applied, a significant improvement in model performance is achieved. Additionally, by applying a data fusion-based transfer learning approach, better results are achieved than previously published work on this topic.INDEX TERMS Head pose estimation, synthetic face, face dataset, visual domain adaptation.

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Section: ) Sasementioning

confidence: 99%

Learning 3D Head Pose From Synthetic Data: A Semi-Supervised Approach

et al. 2021

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“…It covers head poses with large variations, different illumination and occlusion conditions, and thus it can be used as a prime target for evaluation of head pose estimators. We also test our method on some datasets captured by RGB-D cameras like BIWI [5] and SASE [13]. The BIWI dataset which captured in a laboratory setting contains 24 videos with 15,678 frames, generated from RGB-D videos captured by a Kinect device for different subjects and head poses.…”

Section: A Datasetsmentioning

confidence: 99%

Improving Head Pose Estimation with a Combined Loss and Bounding Box Margin Adjustment

Shao

Sun

Özay

et al. 2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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We address a problem of estimating pose of a person's head from its RGB image. The employment of CNNs for the problem has contributed to significant improvement in accuracy in recent works. However, we show that the following two methods, despite their simplicity, can attain further improvement: (i) proper adjustment of the margin of bounding box of a detected face, and (ii) choice of loss functions. We show that the integration of these two methods achieve the new state-of-the-art on standard benchmark datasets for in-the-wild head pose estimation. The Tensorflow implementation of our work is available at https://github.com/ MingzhenShao/HeadPose

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“…For comparison with the 3D databases, an RGB-D sample has been provided in Figure 9. One of the newer databases, the iCV SASE [118] database, is RGB-D dataset solely dedicated to headpose with free facial expressions.…”

Section: Miscellaneous Databasesmentioning

confidence: 99%

Review on Emotion Recognition Databases

Haamer¹,

Rusadze²,

Lüsi³

et al. 2018

Human-Robot Interaction - Theory and Application

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Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format.

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SASE: RGB-Depth Database for Human Head Pose Estimation

Cited by 21 publications

References 33 publications

Learning 3D Head Pose From Synthetic Data: A Semi-Supervised Approach

Learning 3D Head Pose From Synthetic Data: A Semi-Supervised Approach

Improving Head Pose Estimation with a Combined Loss and Bounding Box Margin Adjustment

Review on Emotion Recognition Databases

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