scholarly journals Deep-cARe: Projection-Based Home Care Augmented Reality System with Deep Learning for Elderly

2019 ◽  
Vol 9 (18) ◽  
pp. 3897
Author(s):  
Yoon Jung Park ◽  
Hyocheol Ro ◽  
Nam Kyu Lee ◽  
Tack-Don Han
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Augmented Reality ◽  
Object Detection ◽  
Pose Estimation ◽  
Dimensional Space ◽  
The Elderly ◽  
Small Object ◽  
Learning Module ◽  
The Face

Developing innovative and pervasive smart technologies that provide medical support and improve the welfare of the elderly has become increasingly important as populations age. Elderly people frequently experience incidents of discomfort in their daily lives, including the deterioration of cognitive and memory abilities. To provide auxiliary functions and ensure the safety of the elderly in daily living situations, we propose a projection-based augmented reality (PAR) system equipped with a deep-learning module. In this study, we propose three-dimensional space reconstruction of a pervasive PAR space for the elderly. In addition, we propose the application of a deep-learning module to lay the foundation for contextual awareness. Performance experiments were conducted for grafting the deep-learning framework (pose estimation, face recognition, and object detection) onto the PAR technology through the proposed hardware for verification of execution possibility, real-time execution, and applicability. The precision of the face pose is particularly high by pose estimation; it is used to determine an abnormal user state. For face recognition results of whole class, the average detection rate (DR) was 74.84% and the precision was 78.72%. However, for face occlusions, the average DR was 46.83%. It was confirmed that the face recognition can be performed properly if the face occlusion situation is not frequent. By object detection experiment results, the DR increased as the distance from the system decreased for a small object. For a large object, the miss rate increased when the distance between the object and the system decreased. Scenarios for supporting the elderly, who experience degradation in movement and cognitive functions, were designed and realized, constructed using the proposed platform. In addition, several user interfaces (UI) were implemented according to the scenarios regardless of distance between users and the proposed system. In this study, we developed a bidirectional PAR system that provides the relevant information by understanding the user environment and action intentions instead of a unidirectional PAR system for simple information provision. We present a discussion of the possibility of care systems for the elderly through the fusion of PAR and deep-learning frameworks.

scholarly journals Application of a Fast RCNN Based on Upper and Lower Layers in Face Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lin Jiang ◽  
Jia Chen ◽  
Hiroyoshi Todo ◽  
Zheng Tang ◽  
Sicheng Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Object Detection ◽  
Face Detection ◽  
Feature Detection ◽  
Vital Role ◽  
Spatial Transformation ◽  
Detection Technology ◽  
The Face ◽  
Human Faces

With the development of society, deep learning has been widely used in object detection, face recognition, speech recognition, and other fields. Among them, object detection is a popular direction in computer vision and digital image processing, and face detection is a focus of this hot direction. Although face detection technology has gone through a long research stage, it is still considered as one of the more difficult subjects in human feature detection technology. In addition, the face detection technology itself has two sides, imperceptibility and complexity of the environment, and other defects cause the existing technology to be unable to accurately recognize faces of different proportions, obscured and different postures. Therefore, this paper adopts an advanced deep learning method based on machine vision to detect human faces automatically. In order to accurately detect a variety of human faces, a multiscale fast RCNN method based on upper and lower layers (UPL-RCNN) is proposed. The network is composed of spatial affine transformation components and feature region components (ROI). This method plays a vital role in face detection. First of all, multiscale information can be grouped in detection, so as to deal with small areas of the face. Then, the method can use the inspiration of the human visual system to perform contextual reasoning and spatial transformation, including zooming, cutting, and rotating. Through comparative experiments, the analysis results show that this method can not only accurately detect human faces but also has better performance than fast RCNN. Compared with some advanced methods, this method has the advantages of high accuracy, less time consumption, and no correlation mark.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

scholarly journals A Survey of Small Object Detection Based on Deep Learning

2021 ◽  
Author(s):  
Zhang Zhenghua ◽  
Jiang Ling ◽  
Hong Qingqing
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Small Object ◽  
Small Object Detection

scholarly journals Face Recognition Model Using Back Propagation

2018 ◽  
Vol 7 (3.34) ◽  
pp. 237
Author(s):  
R Aswini Priyanka ◽  
C Ashwitha ◽  
R Arun Chakravarthi ◽  
R Prakash
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Face Identification ◽  
Identification System ◽  
Scientific World ◽  
The Face ◽  
Deep Learning Neural Network

In scientific world, Face recognition becomes an important research topic. The face identification system is an application capable of verifying a human face from a live videos or digital images. One of the best methods is to compare the particular facial attributes of a person with the images and its database. It is widely used in biometrics and security systems. Back in old days, face identification was a challenging concept. Because of the variations in viewpoint and facial expression, the deep learning neural network came into the technology stack it’s been very easy to detect and recognize the faces. The efficiency has increased dramatically. In this paper, ORL database is about the ten images of forty people helps to evaluate our methodology. We use the concept of Back Propagation Neural Network (BPNN) in deep learning model is to recognize the faces and increase the efficiency of the model compared to previously existing face recognition models.   

scholarly journals Deep Learning- Based Surveillance System using Face Recognition

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Learning Method ◽  
Security Systems ◽  
The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

A novel two-stage deep learning-based small-object detection using hyperspectral images

2019 ◽  
Vol 26 (6) ◽  
pp. 597-606 ◽  
Author(s):  
Lu Yan ◽  
Masahiro Yamaguchi ◽  
Naoki Noro ◽  
Yohei Takara ◽  
Fuminori Ando
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Hyperspectral Images ◽  
Small Object ◽  
Two Stage ◽  
Small Object Detection

scholarly journals Underwater Object Detection and Pose Estimation using Deep Learning

2019 ◽  
Vol 52 (21) ◽  
pp. 78-81 ◽  
Author(s):  
MyungHwan Jeon ◽  
Yeongjun Lee ◽  
Young-Sik Shin ◽  
Hyesu Jang ◽  
Ayoung Kim
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Pose Estimation ◽  
Underwater Object
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