Accurate Facial Image Parsing at Real-Time Speed

2019 ◽  
Vol 28 (9) ◽  
pp. 4659-4670 ◽  
Author(s):  
Zhen Wei ◽  
Si Liu ◽  
Yao Sun ◽  
Hefei Ling
Keyword(s):  
Real Time ◽  
Image Parsing ◽  
Facial Image

scholarly journals The Extensive Usage of the Facial Image Threshing Machine for Facial Emotion Recognition Performance

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2026
Author(s):  
Jung Hwan Kim ◽  
Alwin Poulose ◽  
Dong Seog Han
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
System Performance ◽  
Autonomous Vehicles ◽  
Confusion Matrix ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Facial Image ◽  
Facial Images

Facial emotion recognition (FER) systems play a significant role in identifying driver emotions. Accurate facial emotion recognition of drivers in autonomous vehicles reduces road rage. However, training even the advanced FER model without proper datasets causes poor performance in real-time testing. FER system performance is heavily affected by the quality of datasets than the quality of the algorithms. To improve FER system performance for autonomous vehicles, we propose a facial image threshing (FIT) machine that uses advanced features of pre-trained facial recognition and training from the Xception algorithm. The FIT machine involved removing irrelevant facial images, collecting facial images, correcting misplacing face data, and merging original datasets on a massive scale, in addition to the data-augmentation technique. The final FER results of the proposed method improved the validation accuracy by 16.95% over the conventional approach with the FER 2013 dataset. The confusion matrix evaluation based on the unseen private dataset shows a 5% improvement over the original approach with the FER 2013 dataset to confirm the real-time testing.

scholarly journals A Perancangan Sistem Pengenalan Wajah Secara Real-Time pada CCTV dengan Metode Eigenface:

2020 ◽  
Vol 2 (2) ◽  
pp. 20-32
Author(s):  
ARDI JAMHARI
Keyword(s):  
Real Time ◽  
Image Recognition ◽  
Test Subject ◽  
Security Level ◽  
Facial Image ◽  
Security Systems ◽  
Image Objects ◽  
Light Levels ◽  
The Face ◽  
High Degree

The development of times and curiosity in a condition become a reason for people to continue to develop security systems at home, one of which is by CCTV. Basically, CCTV security systems only function as recording devices on the scene. Therefore, the security level of the CCTV is still low. For that we need a system that can be a security solution. The system can detect objects in the form of faces as image input. To insert image objects into the system, the system requires a camera. The object detected by the camera will do a matching face with the face image contained in the dataset class. The system is the application of Computer Vision in the security system. Brain memory will provide a picture of a face that we have known before. The analogy can be likened to a machine or device that has the same ability as humans to recognize individuals through facial images. Through this research a comparison of facial image recognition with eigenface algorithm using feature extraction, PCA and LDA will be implemented on a real-time computer platform. The library used in Eigenface is OpenCV. The purpose of this study is to find out which method has a high degree of accuracy in performing facial image recognition by comparing between the two methods used. The problem faced by the author when performing accuracy tests is the different light levels between the dataset and the test subject, and changes in attributes such as hair and beard can affect the resulting accuracy. Based on the test results it is known that the accuracy produced by the Eigenface PCA is better than the LDA eigenface. The best accuracy on eigenface was obtained with a PCA combination of 98.06%.

Feature-based techniques for real-time morphable model facial image analysis

2005 ◽  
Author(s):  
Siddhartha Chaudhuri ◽  
Randhir K. Singh ◽  
Edoardo Charbon
Keyword(s):  
Image Analysis ◽  
Real Time ◽  
Facial Image ◽  
Feature Based ◽  
Morphable Model ◽  
Facial Image Analysis

scholarly journals Face Mask Detection with Raspberry Pi

2021 ◽  
Vol 9 (VI) ◽  
pp. 3451-3458
Author(s):  
G. Pavan Kumar
Keyword(s):  
Real Time ◽  
Close Contact ◽  
Face Mask ◽  
Raspberry Pi ◽  
Single Shot ◽  
Facial Image ◽  
Pixel Resolution ◽  
Infected Area ◽  
Spinal Network ◽  
Respiratory Droplets

In the wake of the COVID-19 epidemic, institutions such as the academy are suffering the most from global closure if the current situation haven’t rectified. COVID-19 also known as Serious Acute Respiratory Syndrome Corona virus-2 is an infectious disease that is transmitted to an infected person who talks, sneezes or coughs through respiratory droplets. This spreads quickly through close contact with anyone with the disease, or by touching objects or the infected area. By wearing a face mask under the jaws covering at crowded places or by frequently hygiene at your palms and by using at the minimum of 70% sanitizers which are based on alcohol is the best method for the against of the COVID-19. In this project we have used it ML, OpenCV and TensorFlow face recognition. This the model can be used for security purposes because of course an app that works well for use. In this way MobilenetV2 using a BN-based layout too lightweight and embedded this model with Raspberry pi to make real-time mask discovery, when, SSD (Single Shot Detector) format is used and the spinal network is light. As technology advances, Deep Learning has demonstrated its effectiveness in recognition and classification through image processing. The study uses in-depth reading techniques to distinguish facial recognition and to determine whether a person is wearing a facemask or not. The collected data contains 25,000 images using 224x224 pixel resolution and obtained 96% accuracy with the performance of a trained model. The system enhances the Raspberry Pi-based real-time recognition made by alarms and takes a facial image when the person found is not wearing a facemask. This study is beneficial in combating the spread of the virus and in avoiding contact with it.

Some Recent Results in Quiescent Prominence Spectrometry

1979 ◽  
Vol 44 ◽  
pp. 41-47
Author(s):  
Donald A. Landman
Keyword(s):  
Real Time ◽  
Computer System ◽  
Spectral Response ◽  
Absolute Intensity ◽  
Solar Observatory ◽  
Target Size ◽  
Small Target ◽  
Signal Averaging ◽  
Quiescent Prominence

This paper describes some recent results of our quiescent prominence spectrometry program at the Mees Solar Observatory on Haleakala. The observations were made with the 25 cm coronagraph/coudé spectrograph system using a silicon vidicon detector. This detector consists of 500 contiguous channels covering approximately 6 or 80 Å, depending on the grating used. The instrument is interfaced to the Observatory’s PDP 11/45 computer system, and has the important advantages of wide spectral response, linearity and signal-averaging with real-time display. Its principal drawback is the relatively small target size. For the present work, the aperture was about 3″ × 5″. Absolute intensity calibrations were made by measuring quiet regions near sun center.

Sign in / Sign up

Export Citation Format

Share Document