scholarly journals Multiresolution Face Recognition through Virtual Faces Generation Using a Single Image for One Person

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hae-Min Moon ◽  
Min-Gu Kim ◽  
Ju-Hyun Shin ◽  
Sung Bum Pan
Keyword(s):  
Face Recognition ◽  
Home Environment ◽  
Smart Home ◽  
Euclidean Distance ◽  
Recognition Rate ◽  
Time Service ◽  
Surveillance Camera ◽  
Face Images ◽  
Smart Home Environment ◽  
Facial Images

In recent years, various studies have been conducted to provide a real-time service based on face recognition in Internet of things environments such as in a smart home environment. In particular, face recognition in a network-based surveillance camera environment can significantly change the performance or utilization of face recognition technology because the size of image information to be transmitted varies depending on the communication capabilities. In this paper, we propose a multiresolution face recognition method that uses virtual facial images by distance as learning to solve the problem of low recognition rate caused by communication, camera, and distance change. Face images for each virtual distance are generated through clarity and image degradation for each resolution, using a single high-resolution face image. The proposed method achieved a performance that was 5.9% more accurate than methods using MPCA and SVM, when LDA and the Euclidean distance were employed for a DB that was configured using faces that were acquired from the real environments of five different streets.

Depressed Patients Intelligent Recognition in Smart Home Environment

2021 ◽  
Vol 11 (2) ◽  
pp. 353-359
Author(s):  
Jie Zhang ◽  
Tingting Zhao ◽  
Yuan Liu
Keyword(s):  
Home Environment ◽  
Smart Home ◽  
Recognition Rate ◽  
Mental Illnesses ◽  
Support Vector ◽  
Data Sets ◽  
Future Technology ◽  
Duration Of Illness ◽  
Smart Home Environment ◽  
Intelligent Recognition

Depression is one of the most harmful diseases in society today, and the etiology and pathological mechanism of depression is one of the most complicated mental illnesses. As the population of people with depression grows, the patient's long duration of illness and the harmfulness of the results make the disease the biggest challenge in the diagnosis of mental illness. How to improve the recognition rate of depression and make diagnosis and treatment as early as possible is the most effective way. According to the clinical medical manifestations of patients with depression, it is found that there is a very obvious difference between the patients with depression and the normal group in terms of speech characteristics, such as lower tones and slower speech speed. Therefore, this paper proposes a method for intelligent recognition of depression based on speech signals in combination with the contemporary smart home environment. A novel ensemble support vector machine (ESVM) algorithm is proposed in this article, which is applied to several classic depression speech data sets. The organic combination of depression recognition and smart home environment can adapt to the development of future technology.

Depressed Patients Intelligent Recognition in Smart Home Environment

2021 ◽  
Vol 11 (2) ◽  
pp. 353-359
Author(s):  
Jie Zhang ◽  
Tingting Zhao ◽  
Yuan Liu
Keyword(s):  
Home Environment ◽  
Smart Home ◽  
Recognition Rate ◽  
Mental Illnesses ◽  
Support Vector ◽  
Data Sets ◽  
Future Technology ◽  
Duration Of Illness ◽  
Smart Home Environment ◽  
Intelligent Recognition

Depression is one of the most harmful diseases in society today, and the etiology and pathological mechanism of depression is one of the most complicated mental illnesses. As the population of people with depression grows, the patient's long duration of illness and the harmfulness of the results make the disease the biggest challenge in the diagnosis of mental illness. How to improve the recognition rate of depression and make diagnosis and treatment as early as possible is the most effective way. According to the clinical medical manifestations of patients with depression, it is found that there is a very obvious difference between the patients with depression and the normal group in terms of speech characteristics, such as lower tones and slower speech speed. Therefore, this paper proposes a method for intelligent recognition of depression based on speech signals in combination with the contemporary smart home environment. A novel ensemble support vector machine (ESVM) algorithm is proposed in this article, which is applied to several classic depression speech data sets. The organic combination of depression recognition and smart home environment can adapt to the development of future technology.

IntelliHome: An internet of things‐based system for electrical energy saving in smart home environment

2019 ◽  
Vol 36 (1) ◽  
pp. 203-224 ◽  
Author(s):  
Mario A. Paredes‐Valverde ◽  
Giner Alor‐Hernández ◽  
Jorge L. García‐Alcaráz ◽  
María del Pilar Salas‐Zárate ◽  
Luis O. Colombo‐Mendoza ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Energy Saving ◽  
Home Environment ◽  
Smart Home ◽  
Electrical Energy ◽  
Smart Home Environment

A Context-Aware Information Model for Elderly Homecare Services in a Smart Home

2009 ◽  
Author(s):  
Feng Zhou ◽  
Jianxin Roger Jiao ◽  
Songlin Chen ◽  
Daqing Zhang
Keyword(s):  
Home Environment ◽  
Smart Home ◽  
Rough Set Theory ◽  
Research Problem ◽  
Information Model ◽  
Context Aware ◽  
Sensor Platform ◽  
Smart Home Environment ◽  
Manual Assistance

One of the critical situations facing the society across the globe is the problem of elderly homecare services (EHS) due to the aggravation of the society coupled with diseases and limited social resources. This problem has been typically dealt with by manual assistance from caregivers and/or family members. The emerging Ambience Intelligence (AmI) technology suggests itself to be of great potential for EHS applications, owing to its strength in constructing a pervasive computing environment that is sensitive and responsive to the presence of human users. The key challenge of AmI implementation lies in context awareness, namely how to align with the specific decision making scenarios of particular EHS applications. This paper proposes a context-aware information model in a smart home to tackle the EHS problem. Mainly, rough set theory is applied to construct user activity models for recognizing various activities of daily living (ADLs) based on the sensor platform constructed in a smart home environment. Subsequently, issues of case comprehension and homecare services are also discussed. A case study in the smart home environment is presented. Initial findings from the case study suggest the importance of the research problem, as well as the feasibility and potential of the proposed framework.

Sign in / Sign up

Export Citation Format

Share Document