Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild

Kulon, Dominik; Alp, Güler, Riza; Kokkinos, Iasonas; Bronstein, Michael M.; Zafeiriou, Stefanos

doi:10.48550/arxiv.2004.01946

Cited by 1 publication

(1 citation statement)

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“…Hand gesture recognition approaches can also be separated in techniques that use 3D hand models such as MANO hand model [35] or 2D images. Current approaches in 3D hand gesture recognition attempt to regress the 3D model shape and pose parameters [6,22]. However, 3D methods rely on guidance from 2D keypoint detection to regress the model parameters, therefore in situations such as segmented hand gestures of binary images, 3D models are deemed inappropriate.…”

Section: Hand Gesture Recognitionmentioning

confidence: 99%

A novel shape matching descriptor for real-time hand gesture recognition

Lazarou¹,

Li²,

Stathaki³

2021

Preprint

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The current state-of-the-art hand gesture recognition methodologies heavily rely in the use of machine learning. However there are scenarios that machine learning cannot be applied successfully, for example in situations where data is scarce. This is the case when one-to-one matching is required between a query and a database of hand gestures where each gesture represents a unique class. In situations where learning algorithms cannot be trained, classic computer vision techniques such as feature extraction can be used to identify similarities between objects. Shape is one of the most important features that can be extracted from images, however the most accurate shape matching algorithms tend to be computationally inefficient for real-time applications. In this work we present a novel shape matching methodology for real-time hand gesture recognition. Extensive experiments were carried out comparing our method with other shape matching methods with respect to accuracy and computational complexity using our own collected hand gesture database as well as the MPEG-7 database that is widely used for comparing 2D shape matching algorithms. Our method outperforms the other methods and provides the optimal combination of accuracy and computational efficiency for real-time applications.

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Section: Hand Gesture Recognitionmentioning

confidence: 99%