Analysis of FPGA accelerator architecture for Fast Statistical Convolutional Neural Network in real time Emotional Recognition System

2021 ◽  
Vol 1 (7) ◽  
pp. 134-136
Author(s):  
J. Immanuel
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Recognition System ◽  
Emotional Recognition

scholarly journals Face Recognition Using the Convolutional Neural Network for Barrier Gate System

2021 ◽  
Vol 15 (10) ◽  
pp. 138
Author(s):  
Mochammad Langgeng Prasetyo ◽  
Achmad Teguh Wibowo ◽  
Mujib Ridwan ◽  
Mohammad Khusnu Milad ◽  
Sirajul Arifin ◽  
...  
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Recognition System ◽  
Average Error ◽  
The Real ◽  
The Face ◽  
Gate System ◽  
Recognition Technique

The implementation of face recognition technique using CCTV is able to prevent unauthorized person enter the gate. Face recognition can be used for authentication, which can be implemented for preventing of criminal incidents. This re-search proposed a face recognition system using convolutional neural network to open and close the real-time barrier gate. The process consists of a convolutional layer, pooling layer, max pooling, flattening, and fully connected layer for detecting a face. The information was sent to the microcontroller using Internet of Thing (IoT) for controlling the barrier gate. The face recognition results are used to open or close the gate in the real time. The experimental results obtained average error rate of 0.320 and the accuracy of success rate is about 93.3%. The average response time required by microcontroller is about 0.562ms. The simulation result show that the face recognition technique using CNN is highly recommended to be implemented in barrier gate system.

scholarly journals Recognition of Face Emotion using Convolutional Neural Network

2020 ◽  
Vol 9 (9) ◽  
pp. 364-376
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Real Time ◽  
Research Work ◽  
Recognition System ◽  
Video Stream ◽  
Machine Learning Algorithms ◽  
Facial Emotion Recognition ◽  
Facial Emotion

Recognition of face emotion has been a challenging task for many years. This work uses machine learning algorithms for both, a real-time image or a stored database image in the area of facial emotion recognition system. So it is very clear that, deep learning technology becomes important for Human-computer interaction (HCI) applications. The proposed system has two parts, real-time based facial emotion recognition system and also the image based facial emotion recognition system. A Convolutional Neural Network (CNN) model is used to train and test different facial emotion images in this research work. This work was executed successfully using Python 3.7.6 platform. The input Face image of a person was taken using the webcam video stream or from the standard database available for research. The five different facial emotions considered in this work are happy, surprise, angry, sad and neutral. The best recognition accuracy with the proposed system for the webcam video stream is found to be 91.2%, whereas for the input database images is found to be 90.08%.

scholarly journals A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real Datasets

TEM Journal ◽  
2020 ◽  
pp. 937-943
Author(s):  
Rasha Amer Kadhim ◽  
Muntadher Khamees
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
American Sign Language ◽  
Real Time ◽  
Sign Language ◽  
Recognition System ◽  
High Accuracy ◽  
American Sign ◽  
Language Recognition ◽  
Sign Language Recognition

In this paper, a real-time ASL recognition system was built with a ConvNet algorithm using real colouring images from a PC camera. The model is the first ASL recognition model to categorize a total of 26 letters, including (J & Z), with two new classes for space and delete, which was explored with new datasets. It was built to contain a wide diversity of attributes like different lightings, skin tones, backgrounds, and a wide variety of situations. The experimental results achieved a high accuracy of about 98.53% for the training and 98.84% for the validation. As well, the system displayed a high accuracy for all the datasets when new test data, which had not been used in the training, were introduced.

scholarly journals Implementation of an Obstacle Recognition System for the Blind

2019 ◽  
Vol 10 (1) ◽  
pp. 282 ◽  
Author(s):  
Soobin Ou ◽  
Huijin Park ◽  
Jongwoo Lee
Keyword(s):  
Neural Network ◽  
Remote Control ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Support Systems ◽  
Acoustic Signals ◽  
Recognition System ◽  
Single Shot ◽  
Program Interface

The blind encounter commuting risks, such as failing to recognize and avoid obstacles while walking, but protective support systems are lacking. Acoustic signals at crosswalk lights are activated by button or remote control; however, these signals are difficult to operate and not always available (i.e., broken). Bollards are posts installed for pedestrian safety, but they can create dangerous situations in that the blind cannot see them. Therefore, we proposed an obstacle recognition system to assist the blind in walking safely outdoors; this system can recognize and guide the blind through two obstacles (crosswalk lights and bollards) with image training from the Google Object Detection application program interface (API) based on TensorFlow. The recognized results notify the blind through voice guidance playback in real time. The single shot multibox detector (SSD) MobileNet and faster region-convolutional neural network (R-CNN) models were applied to evaluate the obstacle recognition system; the latter model demonstrated better performance. Crosswalk lights were evaluated and found to perform better during the day than night. They were also analyzed to determine if a client could cross at a crosswalk, while the locations of bollards were analyzed by algorithms to guide the client by voice guidance.

scholarly journals Real-Time Underwater Image Recognition with FPGA Embedded System for Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 350 ◽  
Author(s):  
Minghao Zhao ◽  
Chengquan Hu ◽  
Fenglin Wei ◽  
Kai Wang ◽  
Chong Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Embedded System ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
Image Data ◽  
Recognition System ◽  
Frame Rate ◽  
High Definition ◽  
High Flexibility

The underwater environment is still unknown for humans, so the high definition camera is an important tool for data acquisition at short distances underwater. Due to insufficient power, the image data collected by underwater submersible devices cannot be analyzed in real time. Based on the characteristics of Field-Programmable Gate Array (FPGA), low power consumption, strong computing capability, and high flexibility, we design an embedded FPGA image recognition system on Convolutional Neural Network (CNN). By using two technologies of FPGA, parallelism and pipeline, the parallelization of multi-depth convolution operations is realized. In the experimental phase, we collect and segment the images from underwater video recorded by the submersible. Next, we join the tags with the images to build the training set. The test results show that the proposed FPGA system achieves the same accuracy as the workstation, and we get a frame rate at 25 FPS with the resolution of 1920 × 1080. This meets our needs for underwater identification tasks.

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