![]() In: The visual computer, vol 34, pp 1053–1063ĭarrell T, Pentland A (1993) Space-time gestures. Image Video Comput 21(8):745–758Ĭhunyong M, Wang A, Chen G, Xu C (2018) Hand joints-based gesture recognition for noisy dataset using nested interval unscented Kalman filter with LSTM network. In: Smart tools and apps for graphic eurographics associationĬhen F, Fu C, Huang C (2003) Hand gesture recognition using a real-time tracking method and hidden markov models. In: Journal of Pattern Recognition, vol 76, pp 80–94Ĭaputo FM, Prebianca P, Carcangiu A, Spano LD, Giachetti A (2017) A 3 Cent Recognizer: Simple and Effective Retrieval and Classification of Mid-air Gestures from Single 3D Traces. We perform experiments on standard datasets and the results show that we have achieved recognition accuracy comparable to the state-of-the-art.Ĭabido R, Pantrigo J, Montemayor AS, Núnez JC, Vélez JF (2018) Convolutional Neural Networks and Long Short-Term Memory for skeleton-based human activity and hand gesture recognition. We apply Grassmann manifold based discriminant analysis for recognizing each test gesture. These gesture subspaces lie on a Grassmann manifold and capture the intra-class variations and increase the inter-class discriminatory power. Then geometrical features are extracted and a unique gesture subspaces created using SVD for each feature vector matrix of each gesture set. If skeleton data is available we use skeleton information to detect the fingertip in each frame. ![]() In fingertip detection using depth data first we detect the hand using the depth data and used hand shape properties such as finger thickness, finger length, finger width and finger orientation angle to find the shape of the hand. We use depth data or skeleton information to detect the fingertip and store the fingertip points to create the trajectory. It finds various applications such as human computer interaction, ambient assisted living, automated driver assisted systems. Automated dynamic hand gesture recognition is important for improving man-machine communication and understanding human behavior. In this paper, we propose a novel Grassmann manifold based framework for dynamic hand gesture recognition from depth data.
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