Template selection based superpixel earth mover's distance algorithm for hand gesture recognition

Hand gesture recognition is of great importance for human-computer interaction (HCI), because of its extensive applications in virtual reality, sign language recognition, and computer games. Despite lots of previous work, traditional vision-based hand gesture recognition methods are still far from satisfactory for real-life applications. Because of the nature of optical sensing, the quality of the captured images is sensitive to lighting conditions and cluttered backgrounds, thus optical sensor based methods are usually unable to detect and track the hands robustly, which largely affects the performance of hand gesture recognition. Compared to the entire human body, the hand is a smaller object with more complex articulations and more easily affected by segmentation errors. It is thus a very challenging problem to recognize hand gestures. This work focuses on building a robust part-based hand gesture recognition system. To handle the noisy hand shapes obtained from digital camera, we propose a novel distance metric, Finger-Earth Mover’s Distance (FEMD), to measure the dissimilarity between hand shapes. As it only matches the finger parts while not the whole hand, it can better distinguish the hand gestures of slight differences. The experiments demonstrate that proposed hand gesture recognition system’s mean accuracy is 80.4% which is measured on 6 gesture database.

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Hand gesture recognition based on canonical formed superpixel earth mover's distance

2016 IEEE International Conference on Multimedia and Expo (ICME) ◽

10.1109/icme.2016.7552959 ◽

2016 ◽

Cited By ~ 1

Author(s):

Chong Wang ◽

Zhong Liu ◽

Jieyu Zhao

Keyword(s):

Gesture Recognition ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Earth Mover’S Distance ◽

Earth Mover's Distance

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Performance analysis and high recognition rate of automated hand gesture recognition though GMM and SVM-KNN classifiers

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2020/114952020 ◽

2020 ◽

Vol 9 (5) ◽

pp. 7712-7722

Keyword(s):

Performance Analysis ◽

Gesture Recognition ◽

Recognition Rate ◽

Hand Gesture Recognition ◽

Hand Gesture

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Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/4/8 ◽

2020 ◽

Vol 17 (4) ◽

pp. 497-506

Author(s):

Sunil Patel ◽

Ramji Makwana

Keyword(s):

Neural Network ◽

Optical Flow ◽

Gesture Recognition ◽

Hand Gesture Recognition ◽

Classification Model ◽

Hand Gesture ◽

Flow Data ◽

Dynamic Hand Gesture Recognition ◽

Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

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