Global ETD Search

11	Robust recognition of facial expressions on noise degraded facial images Sheikh, Munaf January 2011 (has links) <p>We investigate the use of noise degraded facial images in the application of facial expression recognition. In particular, we trained Gabor+SVMclassifiers to recognize facial expressions images with various types of noise. We applied Gaussian noise, Poisson noise, varying levels of salt and pepper noise, and speckle noise to noiseless facial images. Classifiers were trained with images without noise and then tested on the images with noise. Next, the classifiers were trained using images with noise, and then on tested both images that had noise, and images that were noiseless. Finally, classifiers were tested on images while increasing the levels of salt and pepper in the test set. Our results reflected distinct degradation of recognition accuracy. We also discovered that certain types of noise, particularly Gaussian and Poisson noise, boost recognition rates to levels greater than would be achieved by normal, noiseless images. We attribute this effect to the Gaussian envelope component of Gabor filters being sympathetic to Gaussian-like noise, which is similar in variance to that of the Gabor filters. Finally, using linear regression, we mapped a mathematical model to this degradation and used it to suggest how recognition rates would degrade further should more noise be added to the images.</p> Facial expression recognition Facial action units Noise Gabor Support vector machines Machine learning Poisson probability distribution Gaussian normal distribution Salt and pepper Speckle.
12	Robust recognition of facial expressions on noise degraded facial images Sheikh, Munaf January 2011 (has links) Magister Scientiae - MSc / We investigate the use of noise degraded facial images in the application of facial expression recognition. In particular, we trained Gabor+SVMclassifiers to recognize facial expressions images with various types of noise. We applied Gaussian noise, Poisson noise, varying levels of salt and pepper noise, and speckle noise to noiseless facial images. Classifiers were trained with images without noise and then tested on the images with noise. Next, the classifiers were trained using images with noise, and then on tested both images that had noise, and images that were noiseless. Finally, classifiers were tested on images while increasing the levels of salt and pepper in the test set. Our results reflected distinct degradation of recognition accuracy. We also discovered that certain types of noise, particularly Gaussian and Poisson noise, boost recognition rates to levels greater than would be achieved by normal, noiseless images. We attribute this effect to the Gaussian envelope component of Gabor filters being sympathetic to Gaussian-like noise, which is similar in variance to that of the Gabor filters. Finally, using linear regression, we mapped a mathematical model to this degradation and used it to suggest how recognition rates would degrade further should more noise be added to the images. / South Africa Facial expression recognition Facial action units Noise Gabor Support vector machines Machine learning Poisson probability distribution Gaussian normal distribution Salt and pepper Speckle
13	Facial Expressions as Indicator for Discomfort in Automated Driving Beggiato, Matthias, Rauh, Nadine, Krems, Josef 26 August 2021 (has links) Driving comfort is considered a key factor for broad public acceptance of automated driving. Based on continuous driver/passenger monitoring, potential discomfort could be avoided by adapting automation features such as the driving style. The EU-project MEDIATOR (mediatorproject.eu) aims at developing a mediating system in automated vehicles by constantly evaluating the performance of driver and automation. As facial expressions could be an indicator of discomfort, a driving simulator study has been carried out to investigate this relationship. A total of 41 participants experienced three potentially uncomfortable automated approach situations to a truck driving ahead. The face video of four cameras was analyzed with the Visage facial feature detection and face analysis software, extracting 23 Action Units (AUs). Situation-specific effects showed that the eyes were kept open and eye blinks were reduced (AU43). Inner brows (AU1) as well as upper lids (AU5) raised, indicating surprise. Lips were pressed (AU24) and stretched (AU20) as sign for tension. Overall, facial expression analysis could contribute to detect discomfort in automated driving. info:eu-repo/classification/ddc/100 ddc:100
14	Computational Models of the Production and Perception of Facial Expressions Srinivasan, Ramprakash 07 November 2018 (has links) No description available. Computer Engineering Computer Science Cognitive Psychology Social Psychology Machine Learning Computer Vision Facial Expressions Emotion Action Units Discriminant Analysis Cross-cultural fMRI PCA LDA KSDA Deep Learning Neural Networks SVM Gabor Color

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