Robust Part-Based Hand Gesture Recognition Using Finger-Earth Mover’s Distance

2017 ◽  
Vol 7 (7) ◽  
pp. 131
Author(s):  
Ashwini Kolhe ◽  
R. R. Itkarkar ◽  
Anilkumar V. Nandani
Keyword(s):  
Gesture Recognition ◽  
Computer Games ◽  
Real Life ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Earth Mover’S Distance ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hand Gestures ◽  
Earth Mover's Distance

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.

scholarly journals STUDY OF HAND GESTURE RECOGNITION AND CLASSIFICATION

2017 ◽  
Vol 10 (13) ◽  
pp. 25
Author(s):  
Srinivas K ◽  
Manoj Kumar Rajagopal
Keyword(s):  
Sign Language ◽  
Gesture Recognition ◽  
Hand Movement ◽  
Hand Gesture Recognition ◽  
Hand Movements ◽  
Support Vector ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hand Gestures

To recognize different hand gestures and achieve efficient classification to understand static and dynamic hand movements used for communications.Static and dynamic hand movements are first captured using gesture recognition devices including Kinect device, hand movement sensors, connecting electrodes, and accelerometers. These gestures are processed using hand gesture recognition algorithms such as multivariate fuzzy decision tree, hidden Markov models (HMM), dynamic time warping framework, latent regression forest, support vector machine, and surface electromyogram. Hand movements made by both single and double hands are captured by gesture capture devices with proper illumination conditions. These captured gestures are processed for occlusions and fingers close interactions for identification of right gesture and to classify the gesture and ignore the intermittent gestures. Real-time hand gestures recognition needs robust algorithms like HMM to detect only the intended gesture. Classified gestures are then compared for the effectiveness with training and tested standard datasets like sign language alphabets and KTH datasets. Hand gesture recognition plays a very important role in some of the applications such as sign language recognition, robotics, television control, rehabilitation, and music orchestration.

scholarly journals Pose Invariant Hand Gesture Recognition using Two Stream Transfer Learning Architecture

2019 ◽  
Vol 9 (1) ◽  
pp. 1771-1777
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Gesture Recognition ◽  
Classification Accuracy ◽  
Decision Fusion ◽  
Hand Gesture Recognition ◽  
Machine Learning Techniques ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition

The hand gesture detection problem is one of the most prominent problems in machine learning and computer vision applications. Many machine learning techniques have been employed to solve the hand gesture recognition. These techniques find applications in sign language recognition, virtual reality, human machine interaction, autonomous vehicles, driver assistive systems etc. In this paper, the goal is to design a system to correctly identify hand gestures from a dataset of hundreds of hand gesture images. In order to incorporate this, decision fusion based system using the transfer learning architectures is proposed to achieve the said task. Two pretrained models namely ‘MobileNet’ and ‘Inception V3’ are used for this purpose. To find the region of interest (ROI) in the image, YOLO (You Only Look Once) architecture is used which also decides the type of model. Edge map images and the spatial images are trained using two separate versions of the MobileNet based transfer learning architecture and then the final probabilities are combined to decide upon the hand sign of the image. The simulation results using classification accuracy indicate the superiority of the approach of this paper against the already researched approaches using different quantitative techniques such as classification accuracy.

scholarly journals Hand Gesture Recognition Using PCA, KNN and SVM

2019 ◽  
pp. 547-550
Author(s):  
Julakanti Likhitha Reddy ◽  
Bhavya Mallela ◽  
Lakshmi Lavanya Bannaravuri ◽  
Kotha Mohan Krishna
Keyword(s):  
Gesture Recognition ◽  
Skin Color ◽  
Template Matching ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Color Model ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Skin Color Model

To interact with world using expressions or body movements is comparatively effective than just speaking. Gesture recognition can be a better way to convey meaningful information. Communication through gestures has been widely used by humans to express their thoughts and feelings. Gestures can be performed with any body part like head, face, hands and arms but most predominantly hand is use to perform gestures, Hand Gesture Recognition have been widely accepted for numerous applications such as human computer interactions, robotics, sign language recognition, etc. This paper focuses on bare hand gesture recognition system by proposing a scheme using a database-driven hand gesture recognition based upon skin color model approach and thresholding approach along with an effective template matching with can be effectively used for human robotics applications and similar other applications .Initially, hand region is segmented by applying skin color model in YCbCr color space. Y represents the luminance and Cb and Cr represents chrominance. In the next stage Otsu thresholding is applied to separate foreground and background. Finally, template based matching technique is developed using Principal Component Analysis (PCA), k-nearest neighbour (KNN) and Support Vector Machine (SVM) for recognition. KNN is used for statistical estimation and pattern recognition. SVM can be used for classification or regression problems.

scholarly journals A Deep Learning-Based End-to-End Composite System for Hand Detection and Gesture Recognition

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5282 ◽  
Author(s):  
Adam Ahmed Qaid MOHAMMED ◽  
Jiancheng Lv ◽  
MD. Sajjatul Islam
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hand Detection ◽  
Cluttered Environments ◽  
Classical Dance ◽  
Action Analysis

Recent research on hand detection and gesture recognition has attracted increasing interest due to its broad range of potential applications, such as human-computer interaction, sign language recognition, hand action analysis, driver hand behavior monitoring, and virtual reality. In recent years, several approaches have been proposed with the aim of developing a robust algorithm which functions in complex and cluttered environments. Although several researchers have addressed this challenging problem, a robust system is still elusive. Therefore, we propose a deep learning-based architecture to jointly detect and classify hand gestures. In the proposed architecture, the whole image is passed through a one-stage dense object detector to extract hand regions, which, in turn, pass through a lightweight convolutional neural network (CNN) for hand gesture recognition. To evaluate our approach, we conducted extensive experiments on four publicly available datasets for hand detection, including the Oxford, 5-signers, EgoHands, and Indian classical dance (ICD) datasets, along with two hand gesture datasets with different gesture vocabularies for hand gesture recognition, namely, the LaRED and TinyHands datasets. Here, experimental results demonstrate that the proposed architecture is efficient and robust. In addition, it outperforms other approaches in both the hand detection and gesture classification tasks.

A Review on Vision-Based Hand Gesture Recognition and Applications

2015 ◽  
pp. 256-281 ◽  
Author(s):  
Ananya Choudhury ◽  
Anjan Kumar Talukdar ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Research Area ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Dynamic Background ◽  
Lighting Condition ◽  
Recognition Systems

In the present scenario, vision based hand gesture recognition has become a highly emerging research area for the purpose of human computer interaction. Such recognition systems are deployed to serve as a replacement for the commonly used human-machine interactive devices such as keyboard, mouse, joystick etc. in real world situations. The major challenges faced by a vision based hand gesture recognition system include recognition in complex background, in dynamic background, in presence of multiple gestures in the background, under variable lighting condition, under different viewpoints etc. In the context of sign language recognition, which is a highly demanding application of hand gesture recognition system, coarticulation detection is a challenging task. The main objective of this chapter is to provide a general overview of vision based hand gesture recognition system as well as to bring into light some of the research works that have been done in this field.

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