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 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 Hand Gesture Recognition and Voice Conversion for Hearing and Speech Aided Community

2020 ◽  
pp. 223-228
Author(s):  
Divya K V ◽  
Harish E ◽  
Nikhil Jain D ◽  
Nirdesh Reddy B
Keyword(s):  
Sign Language ◽  
Gesture Recognition ◽  
Feature Detection ◽  
Hand Gesture Recognition ◽  
Voice Conversion ◽  
Support Vector ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Mlp Classifier

Sign language recognition (SLR) aims to interpret sign languages automatically by a computer in order to help the deaf communicate with hearing society conveniently. Our aim is to design a system to help the person who trained the hearing impaired to communicate with the rest of the world using sign language or hand gesture recognition techniques. In this system, feature detection and feature extraction of hand gesture is done with the help of Support Vector Machine (SVM), K-Neighbors-Classifier, Logistic-Regression, MLP-Classifier, Naive Bayes, Random-Forest-Classifier algorithms are using image processing.

scholarly journals Sign Language Recognition System for Disabled People

2019 ◽  
Vol 9 (1S) ◽  
pp. 48-52
Keyword(s):  
Real Time ◽  
Sign Language ◽  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Real Time System

The aim is to present a real time system for hand gesture recognition on the basis of detection of some meaningful shape based feature like orientation, center of mass, status of fingers in term of raised or folded fingers of hand and their respective location in image. Hand gesture Recognition System has various real time applications in natural, innovative, user friendly way of how to interact with the computer which has more facilities that are familiar to us. Gesture recognition has a wide area of application including Human machine interaction, sign language, game technology robotics etc are some of the areas where Gesture recognition can be applied. More specifically hand gesture is used as a signal or input means given to the computer especially by disabled person. Being an interesting part of the human and computer interaction hand gesture recognition is needed for real life application, but complex of structures presents in human hand has a lot of challenges for being tracked and extracted. Making use of computer vision algorithms and gesture recognition techniques will result in developing low-cost interface devices using hand gestures for interacting with objects in virtual environment. SVM (support vector machine) and efficient feature extraction technique is presented for hand gesture recognition. This method deals with the dynamic aspects of hand gesture recognition system.

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 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.

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 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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