scholarly journals Deep learning based facial expressions recognition system for assisting visually impaired persons

2020 ◽  
Vol 9 (3) ◽  
pp. 1208-1219
Author(s):  
Hendra Kusuma ◽  
Muhammad Attamimi ◽  
Hasby Fahrudin
Keyword(s):  
Deep Learning ◽  
Facial Expressions ◽  
Visually Impaired ◽  
Learning Algorithm ◽  
Recognition System ◽  
Verbal Information ◽  
Deep Learning Algorithm ◽  
Facial Recognition System ◽  
Visually Impaired Persons ◽  
Facial Expressions Recognition

In general, a good interaction including communication can be achieved when verbal and non-verbal information such as body movements, gestures, facial expressions, can be processed in two directions between the speaker and listener. Especially the facial expression is one of the indicators of the inner state of the speaker and/or the listener during the communication. Therefore, recognizing the facial expressions is necessary and becomes the important ability in communication. Such ability will be a challenge for the visually impaired persons. This fact motivated us to develop a facial recognition system. Our system is based on deep learning algorithm. We implemented the proposed system on a wearable device which enables the visually impaired persons to recognize facial expressions during the communication. We have conducted several experiments involving the visually impaired persons to validate our proposed system and the promising results were achieved.

scholarly journals INFERRING THE SCALE AND CONTENT OF A MAP USING DEEP LEARNING

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 17-24
Author(s):  
G. Touya ◽  
F. Brisebard ◽  
F. Quinton ◽  
A. Courtial
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Visually Impaired ◽  
The Other ◽  
Visually Impaired People ◽  
Impaired People ◽  
Tactile Maps

Abstract. Visually impaired people cannot use classical maps but can learn to use tactile relief maps. These tactile maps are crucial at school to learn geography and history as well as the other students. They are produced manually by professional transcriptors in a very long and costly process. A platform able to generate tactile maps from maps scanned from geography textbooks could be extremely useful to these transcriptors, to fasten their production. As a first step towards such a platform, this paper proposes a method to infer the scale and the content of the map from its image. We used convolutional neural networks trained with a few hundred maps from French geography textbooks, and the results show promising results to infer labels about the content of the map (e.g. ”there are roads, cities and administrative boundaries”), and to infer the extent of the map (e.g. a map of France or of Europe).

Indoor Navigation Aid Systems for the Blind and Visually Impaired Based on Depth Sensors

2021 ◽  
pp. 187-223
Author(s):  
Fernando Merchan ◽  
Martin Poveda ◽  
Danilo E. Cáceres-Hernández ◽  
Javier E. Sanchez-Galan
Keyword(s):  
Deep Learning ◽  
Visually Impaired ◽  
Indoor Navigation ◽  
Assistive Technologies ◽  
Microsoft Kinect ◽  
Depth Sensors ◽  
Blind And Visually Impaired ◽  
Recent Developments ◽  
Depth Vision

This chapter focuses on the contributions made in the development of assistive technologies for the navigation of blind and visually impaired (BVI) individuals. A special interest is placed on vision-based systems that make use of image (RGB) and depth (D) information to assist their indoor navigation. Many commercial RGB-D cameras exist on the market, but for many years the Microsoft Kinect has been used as a tool for research in this field. Therefore, first-hand experience and advances on the use of Kinect for the development of an indoor navigation aid system for BVI individuals is presented. Limitations that can be encountered in building such a system are addressed at length. Finally, an overview of novel avenues of research in indoor navigation for BVI individuals such as integration of computer vision algorithms, deep learning for the classification of objects, and recent developments with stereo depth vision are discussed.

scholarly journals Enhancing perception for the visually impaired with deep learning techniques and low-cost wearable sensors

2020 ◽  
Vol 137 ◽  
pp. 27-36 ◽  
Author(s):  
Zuria Bauer ◽  
Alejandro Dominguez ◽  
Edmanuel Cruz ◽  
Francisco Gomez-Donoso ◽  
Sergio Orts-Escolano ◽  
...  
Keyword(s):  
Deep Learning ◽  
Visually Impaired ◽  
Low Cost ◽  
Wearable Sensors ◽  
Learning Techniques

scholarly journals Deep Learning for Retail Product Recognition: Challenges and Techniques

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Yuchen Wei ◽  
Son Tran ◽  
Shuxiang Xu ◽  
Byeong Kang ◽  
Matthew Springer
Keyword(s):  
Deep Learning ◽  
Visually Impaired ◽  
Retail Store ◽  
Social Progress ◽  
Time Saving ◽  
Daily Lives ◽  
Manual Operation ◽  
Comprehensive Literature Review ◽  
Public Datasets ◽  
Automatic Product

Taking time to identify expected products and waiting for the checkout in a retail store are common scenes we all encounter in our daily lives. The realization of automatic product recognition has great significance for both economic and social progress because it is more reliable than manual operation and time-saving. Product recognition via images is a challenging task in the field of computer vision. It receives increasing consideration due to the great application prospect, such as automatic checkout, stock tracking, planogram compliance, and visually impaired assistance. In recent years, deep learning enjoys a flourishing evolution with tremendous achievements in image classification and object detection. This article aims to present a comprehensive literature review of recent research on deep learning-based retail product recognition. More specifically, this paper reviews the key challenges of deep learning for retail product recognition and discusses potential techniques that can be helpful for the research of the topic. Next, we provide the details of public datasets which could be used for deep learning. Finally, we conclude the current progress and point new perspectives to the research of related fields.

Sign in / Sign up

Export Citation Format

Share Document