scholarly journals Gesture-Based Human Machine Interaction Using RCNNs in Limited Computation Power Devices

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8202
Author(s):  
Alberto Tellaeche Iglesias ◽  
Ignacio Fidalgo Astorquia ◽  
Juan Ignacio Vázquez Gómez ◽  
Surajit Saikia
Keyword(s):  
Detection System ◽  
Recognition System ◽  
Power Devices ◽  
Human Machine Interaction ◽  
Lighting Conditions ◽  
Learning Techniques ◽  
Environmental Variations ◽  
Advanced Robotics ◽  
Machine Interaction ◽  
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The use of gestures is one of the main forms of human machine interaction (HMI) in many fields, from advanced robotics industrial setups, to multimedia devices at home. Almost every gesture detection system uses computer vision as the fundamental technology, with the already well-known problems of image processing: changes in lighting conditions, partial occlusions, variations in color, among others. To solve all these potential issues, deep learning techniques have been proven to be very effective. This research proposes a hand gesture recognition system based on convolutional neural networks and color images that is robust against environmental variations, has a real time performance in embedded systems, and solves the principal problems presented in the previous paragraph. A new CNN network has been specifically designed with a small architecture in terms of number of layers and total number of neurons to be used in computationally limited devices. The obtained results achieve a percentage of success of 96.92% on average, a better score than those obtained by previous algorithms discussed in the state of the art.

scholarly journals Automatic Multiface Expression Recognition Using Convolutional Neural Network

2021 ◽  
Vol 11 (2) ◽  
pp. 1-13
Author(s):  
Padmapriya K.C. ◽  
Leelavathy V. ◽  
Angelin Gladston
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Behavior Understanding ◽  
Human Behavior Understanding ◽  
Expression Classification ◽  
Machine Interaction

The human facial expressions convey a lot of information visually. Facial expression recognition plays a crucial role in the area of human-machine interaction. Automatic facial expression recognition system has many applications in human behavior understanding, detection of mental disorders and synthetic human expressions. Recognition of facial expression by computer with high recognition rate is still a challenging task. Most of the methods utilized in the literature for the automatic facial expression recognition systems are based on geometry and appearance. Facial expression recognition is usually performed in four stages consisting of pre-processing, face detection, feature extraction, and expression classification. In this paper we applied various deep learning methods to classify the seven key human emotions: anger, disgust, fear, happiness, sadness, surprise and neutrality. The facial expression recognition system developed is experimentally evaluated with FER dataset and has resulted with good accuracy.

scholarly journals A face recognition system using convolutional feature extraction with linear collaborative discriminant regression classification

2022 ◽  
Vol 12 (2) ◽  
pp. 1468
Author(s):  
Sangamesh Hosgurmath ◽  
Viswanatha Vanjre Mallappa ◽  
Nagaraj B. Patil ◽  
Vishwanath Petli
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Distance Ratio ◽  
Research Areas ◽  
Lighting Conditions ◽  
Learning Techniques ◽  
Face Recognition System ◽  
The Face

Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in convolutional neural network (CNN) and also used to make the model robust than other traditional feature extraction technique called local multiple pattern (LMP). The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the features inside the class reduces. The results stated that the CNN-LCDRC achieved 93.10% and 87.60% of mean recognition accuracy, where traditional LCDRC achieved 83.35% and 77.70% of mean recognition accuracy on ORL and YALE databases respectively for the training number 8 (i.e. 80% of training and 20% of testing data).

ADDRESSING THE PROBLEMS OF DETECTING FACES WITH NEURAL NETWORKS

2007 ◽  
Vol 07 (04) ◽  
pp. 617-640 ◽  
Author(s):  
KEVIN CURRAN ◽  
NEIL McCAUGHLEY ◽  
XUELONG LI
Keyword(s):  
Face Detection ◽  
Detection System ◽  
Pedestrian Detection ◽  
Image Sequences ◽  
Database Management System ◽  
Surveillance Systems ◽  
Human Computer Interface ◽  
Human Machine Interaction ◽  
The Face ◽  
Machine Interaction

The face is the most distinctive and widely used key to a person's identity. The area of face detection has attracted considerable attention in the advancement of human-machine interaction as it provides a natural and efficient way to communicate between humans and machines. The problem of facial parts in image sequences has become a popular area of research due to emerging applications in intelligent human-computer interface, surveillance systems, content-based image retrieval, video conferencing, financial transaction, forensic applications, pedestrian detection, image database management system and so on. This paper presents the results of an image based neural network face detection system which seeks to address the problem of detecting faces under gross variations.

scholarly journals ONLINE AND OFFLINE CONTROL OF COLLABORATIVE ROBOTS USED MIXED REALITY

2021 ◽  
Vol 7 (2) ◽  
pp. 61-66
Author(s):  
Jozef Husár ◽  
Lucia Knapčíková
Keyword(s):  
Mixed Reality ◽  
Current Trend ◽  
Manufacturing Companies ◽  
Human Machine Interaction ◽  
Collaborative Robots ◽  
Current Trends ◽  
Smart Glasses ◽  
Microsoft Hololens ◽  
Advanced Robotics ◽  
Machine Interaction

The presented article points to the combination of mixed reality with advanced robotics and manipulators. It is a current trend and synonymous with the word industry 5.0, where human-machine interaction is an important element. This element is collaborative robots in cooperation with intelligent smart glasses. In the article, we gradually defined the basic elements of the investigated system. We showed how to operate them to control a collaborative robot online and offline using mixed reality. We pointed out the software and hardware side of a specific design. In the practical part, we provided illustrative examples of a robotic workplace, which was displayed using smart glasses Microsoft HoloLens 2. In conclusion, we can say that the current trends in industry 4.0 significantly affect and accelerate activities in manufacturing companies. Therefore, it is necessary to prepare for the arrival of Industry 5.0, which will focus primarily on collaborative robotics.

Computer control in human-machine interaction systems by hand movements

2021 ◽  
pp. 42-48
Author(s):  
Hoa Tat Thang
Keyword(s):  
Physical Disabilities ◽  
Computer Control ◽  
Recognition System ◽  
Body Language ◽  
Hand Movements ◽  
Hand Tracking ◽  
Human Machine Interaction ◽  
Interactive Applications ◽  
People With Physical Disabilities ◽  
Machine Interaction

Computers have become popular in recent years. The forms of human-computer interaction are increasingly diverse. In many cases, controlling the computer is not only through the mouse and keyboard, but humans must control the computer through body language and representation. For some people with physical disabilities, controlling the computer through hand movements is essential to help them interact with the computer. The field of simulation also needs these interactive applications. This paper studies a solution to build a hand tracking and gesture recognition system that allows cursor movement and corresponding actions with mouse and keyboard. The research team confirms that the system works stably, accurately and can control the computer instead of a conventional mouse and keyboard through the implementation and evaluation.

How Artificial Intelligence, Machine Learning and Deep Learning are Radically Different?

2018 ◽  
Vol 8 (2) ◽  
pp. 1
Author(s):  
Tanya Tiwari ◽  
Tanuj Tiwari ◽  
Sanjay Tiwari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Analysis Software ◽  
Science Data ◽  
Human Machine Interaction ◽  
Learning Techniques ◽  
Nested Hierarchy ◽  
Further Development ◽  
Machine Interaction

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). A computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence has made it possible. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so forth. Machine Learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence. Machine learning (ML)is a vibrant field of research, with a range of exciting areas for further development across different methods and applications. These areas include algorithmic interpretability, robustness, privacy, fairness, inference of causality, human-machine interaction, and security. The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful. Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones. This paper gives an overview of artificial intelligence, machine learning & deep learning techniques and compare these techniques.

scholarly journals A Gesture Recognition Algorithm for Hand-Assisted Laparoscopic Surgery

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5182
Author(s):  
Carmen López-Casado ◽  
Enrique Bauzano ◽  
Irene Rivas-Blanco ◽  
Carlos J. Pérez-del-Pulgar ◽  
Víctor F. Muñoz
Keyword(s):  
Laparoscopic Surgery ◽  
Gesture Recognition ◽  
Hidden Markov ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Human Machine Interaction ◽  
Additional Tool ◽  
Implicit Information ◽  
Robot Assistant ◽  
Machine Interaction

Minimally invasive surgery (MIS) techniques are growing in quantity and complexity to cover a wider range of interventions. More specifically, hand-assisted laparoscopic surgery (HALS) involves the use of one surgeon’s hand inside the patient whereas the other one manages a single laparoscopic tool. In this scenario, those surgical procedures performed with an additional tool require the aid of an assistant. Furthermore, in the case of a human–robot assistant pairing a fluid communication is mandatory. This human–machine interaction must combine both explicit orders and implicit information from the surgical gestures. In this context, this paper focuses on the development of a hand gesture recognition system for HALS. The recognition is based on a hidden Markov model (HMM) algorithm with an improved automated training step, which can also learn during the online surgical procedure by means of a reinforcement learning process.

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