Hand Pose Estimation and Hand Shape Classification Using Multi-layered Randomized Decision Forests

2012 ◽  
pp. 852-863 ◽  
Author(s):  
Cem Keskin ◽  
Furkan Kıraç ◽  
Yunus Emre Kara ◽  
Lale Akarun
Keyword(s):  
Pose Estimation ◽  
Shape Classification ◽  
Hand Pose Estimation ◽  
Hand Shape ◽  
Randomized Decision Forests ◽  
Hand Pose

User-Adaptable Hand Pose Estimation Technique for Human-Robot Interaction

2009 ◽  
Vol 21 (6) ◽  
pp. 739-748 ◽  
Author(s):  
Albert Causo ◽  
◽  
Etsuko Ueda ◽  
Kentaro Takemura ◽  
Yoshio Matsumoto ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Camera System ◽  
Data Glove ◽  
Hand Pose Estimation ◽  
Hand Model ◽  
Hand Size ◽  
Hand Shape ◽  
Hand Pose

Hand pose estimation using a multi-camera system allows natural non-contact interfacing unlike when using bulky data gloves. To enable any user to use the system regardless of gender or physical differences such as hand size, we propose hand model individualization using only multiple cameras. From the calibration motion, our method estimates the finger link lengths as well as the hand shape by minimizing the gap between the hand model and observation. We confirmed the feasibility of our proposal by comparing 1) actual and estimated link lengths and 2) hand pose estimation results using our calibrated hand model, a prior hand model and data obtained from data glove measurements.

Hybrid Approach Based on Machine Learning for Hand Shape and Key Point’s Estimation

2022 ◽  
Author(s):  
Chen Zhongshan ◽  
Feng Xinning ◽  
Oscar Sanjuán Martínez ◽  
Rubén González Crespo
Keyword(s):  
Machine Learning ◽  
Pose Estimation ◽  
High Efficiency ◽  
Learning Algorithm ◽  
Hybrid Approach ◽  
Point Estimation ◽  
Estimation Errors ◽  
Hand Pose Estimation ◽  
Hand Shape ◽  
Hand Pose

In human-computer interaction and virtual truth, hand pose estimation is essential. Public dataset experimental analysis Different biometric shows that a particular system creates low manual estimation errors and has a more significant opportunity for new hand pose estimation activity. Due to the fluctuations, self-occlusion, and specific modulations, the structure of hand photographs is quite tricky. Hence, this paper proposes a Hybrid approach based on machine learning (HABoML) to enhance the current competitiveness, performance experience, experimental hand shape, and key point estimation analysis. In terms of strengthening the ability to make better self-occlusion adjustments and special handshake and poses estimations, the machine learning algorithm is combined with a hybrid approach. The experiment results helped define a set of follow-up experiments for the proposed systems in this field, which had a high efficiency and performance level. The HABoML strategy decreased analysis precision by 9.33% and is a better solution.

scholarly journals Hand Pose Estimation in the Task of Egocentric Actions

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 10533-10547
Author(s):  
Marek Hruz ◽  
Jakub Kanis ◽  
Zdenek Krnoul
Keyword(s):  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Hand Pose

scholarly journals 3D Hand Pose Estimation via Graph-Based Reasoning

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 35824-35833
Author(s):  
Jae-Hun Song ◽  
Suk-Ju Kang
Keyword(s):  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Hand Pose

Hand PointNet-based 3D Hand Pose Estimation in Egocentric RGB-D Images

2020 ◽  
Author(s):  
Van-Hung Le ◽  
Van-Nam Hoang ◽  
Hai Vu ◽  
Thi-Lan Le ◽  
Thanh-Hai Tran ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Hand Pose

scholarly journals Semi-Supervised Joint Learning for Hand Gesture Recognition from a Single Color Image

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1007
Author(s):  
Chi Xu ◽  
Yunkai Jiang ◽  
Jun Zhou ◽  
Yi Liu
Keyword(s):  
Gesture Recognition ◽  
Pose Estimation ◽  
Color Image ◽  
Recognition Performance ◽  
Recognition Task ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Estimation Task ◽  
Hand Pose Estimation ◽  
Hand Pose

Hand gesture recognition and hand pose estimation are two closely correlated tasks. In this paper, we propose a deep-learning based approach which jointly learns an intermediate level shared feature for these two tasks, so that the hand gesture recognition task can be benefited from the hand pose estimation task. In the training process, a semi-supervised training scheme is designed to solve the problem of lacking proper annotation. Our approach detects the foreground hand, recognizes the hand gesture, and estimates the corresponding 3D hand pose simultaneously. To evaluate the hand gesture recognition performance of the state-of-the-arts, we propose a challenging hand gesture recognition dataset collected in unconstrained environments. Experimental results show that, the gesture recognition accuracy of ours is significantly boosted by leveraging the knowledge learned from the hand pose estimation task.

scholarly journals Robust hand gesture recognition using multiple shape-oriented visual cues

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Samy Bakheet ◽  
Ayoub Al-Hamadi
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Pose Estimation ◽  
Depth Map ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Hand Pose Estimation ◽  
Time Operation ◽  
Hand Pose

AbstractRobust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape cues. The framework incorporates a specific module for hand pose estimation based on depth map data, where the hand silhouette is first extracted from the extremely detailed and accurate depth map captured by a time-of-flight (ToF) depth sensor. A hybrid multi-modal descriptor that integrates multiple affine-invariant boundary-based and region-based features is created from the hand silhouette to obtain a reliable and representative description of individual gestures. Finally, an ensemble of one-vs.-all support vector machines (SVMs) is independently trained on each of these learned feature representations to perform gesture classification. When evaluated on a publicly available dataset incorporating a relatively large and diverse collection of egocentric hand gestures, the approach yields encouraging results that agree very favorably with those reported in the literature, while maintaining real-time operation.

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