Automatic recognition of facial expressions using microsoft kinect with artificial neural network

2016 ◽  
Author(s):  
Safae Elhoufi ◽  
Maha Jazouli ◽  
Aicha Majda ◽  
Arsalane Zarghili ◽  
Rachid Aalouane
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Facial Expressions ◽  
Automatic Recognition ◽  
Microsoft Kinect ◽  
Recognition Of Facial Expressions ◽  
Artificial Neural

scholarly journals Artificial Neural Network for Vibration Frequency Measurement Using Kinect V2

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Jiantao Liu ◽  
Xiaoxiang Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vibration Frequency ◽  
Optical Measurement ◽  
Frequency Measurement ◽  
Microsoft Kinect ◽  
Depth Information ◽  
Research Attention ◽  
Full Field ◽  
Artificial Neural

Optical measurement can substantially reduce the required amount of labor and simplify the measurement process. Furthermore, the optical measurement method can provide full-field measurement results of the target object without affecting the physical properties of the measurement target, such as stiffness, mass, or damping. The advent of consumer grade depth cameras, such as the Microsoft Kinect, Intel RealSence, and ASUS Xtion, has attracted significant research attention owing to their availability and robustness in sampling depth information. This paper presents an effective method employing the Kinect sensor V2 and an artificial neural network for vibration frequency measurement. Experiments were conducted to verify the performance of the proposed method. The proposed method can provide good frequency prediction within acceptable accuracy compared to an industrial vibrometer, with the advantages of contactless process and easy pipeline implementation.

Automatic recognition of alertness and drowsiness from EEG by an artificial neural network

2002 ◽  
Vol 24 (5) ◽  
pp. 349-360 ◽  
Author(s):  
Aleksandra Vuckovic ◽  
Vlada Radivojevic ◽  
Andrew C.N. Chen ◽  
Dejan Popovic
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Automatic Recognition ◽  
Artificial Neural

scholarly journals Recognition of Human Facial Expressions Using DCT-DWT and Artificial Neural Network

2021 ◽  
pp. 2090-2098
Author(s):  
Wasan. Maddah Alaluosi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Facial Expressions ◽  
Emotional State ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Discrete Wavelet ◽  
Three Stages ◽  
Artificial Neural ◽  
Facial Images

Facial expressions are a term that expresses a group of movements of the facial fore muscles that is related to one's own human emotions. Human–computer interaction (HCI) has been considered as one of the most attractive and fastest-growing fields. Adding emotional expression’s recognition to expect the users’ feelings and emotional state can drastically improves HCI. This paper aims to demonstrate the three most important facial expressions (happiness, sadness, and surprise). It contains three stages; first, the preprocessing stage was performed to enhance the facial images. Second, the feature extraction stage depended on Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) methods. Third, the recognition stage was applied using an artificial neural network, known as Back Propagation Neural Network (BPNN), on database images from Cohen-Kanade. The method was shown to be very efficient, where the total rate of recognition of the three facial expressions was 92.9%.

Method and Device for Automatic Recognition of Unconditional Potato Tubes

2020 ◽  
pp. 196-205
Author(s):  
Volodimir Vetokhin ◽  
◽  
Viktor Goldyban ◽  
M. Kurylovich ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pulse Signal ◽  
Single Pulse ◽  
Automatic Recognition ◽  
Inspection System ◽  
Potato Tubers ◽  
Sorting Machine ◽  
Automatic Sorting ◽  
Artificial Neural

The aim of the article is to improve the quality and productivity of sorting by developing a method and an intelligent device for automatic recognition and inspection of substandard potato tubers. The article describes a prototype of an automatic sorting machine designed to recognize external defects in potato tubers and automatically inspect them with a jet of compressed air. The recognition process consisted of three main modules: segmentation, tracking a potato moving in a frame along a conveyor belt, and classification using a trained artificial neural network. For the segmentation of potato tubers against the background of the transporting conveyor in real time, a method based on the calculation of the color threshold was used. The centroid tracking algorithm was used to track moving potato tubers. To train the artificial neural network, we created our own dataset consisting of images of marketable and defective potato tubers. A prototype of an automatic sorting machine has been developed, which is based on the concept of intelligent data analysis, according to which the images of potato tubers obtained from a video camera are processed and formed into images with subsequent recognition and signaling to the executive device of the automatic inspection system in the form of a single pulse signal when determining the tuber as substandard.

scholarly journals Artificial Neural Network Based Ensemble Approach for Multicultural Facial Expressions Analysis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 134950-134963 ◽  
Author(s):  
Ghulam Ali ◽  
Amjad Ali ◽  
Farman Ali ◽  
Umar Draz ◽  
Fiaz Majeed ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Facial Expressions ◽  
Ensemble Approach ◽  
Artificial Neural

Automatic recognition of vigilance state by using a wavelet-based artificial neural network

2005 ◽  
Vol 14 (1) ◽  
pp. 45-55 ◽  
Author(s):  
Abdulhamit Subasi ◽  
M. Kemal Kiymik ◽  
Mehmet Akin ◽  
Osman Erogul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Automatic Recognition ◽  
Vigilance State ◽  
Artificial Neural
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