Abstract: The innovation disclosed and claimed herein, in aspects thereof, comprises systems and methods of identifying AUs and emotion categories in images. The systems and methods utilized a set of images that include facial images of people. The systems and methods analyze the facial images to determine AUs and facial color due to facial blood flow variations that are indicative of an emotion category. In aspects, the analysis can include Gabor transforms to determine the AUs, AU intensities and emotion categories. In other aspects, the systems and method can include color variance analysis to determine the AUs, AU intensities and emotion categories. In further aspects, the analysis can include convolutional neural networks that are trained to determine the AUs, emotion categories and their intensities.

Abstract: Advancing beyond Interpretability and explainability approaches that may uncover what a Deep Neural Network (DNN) models, i.e., what each node (cell) in the network represents and what images are most likely to activate the model provide a mimicked type of learning of generalization applicable to previously unseen samples. The approach provides an ability to detect and circumvent adversarial attacks, with self-verification and trust-building structural modeling. Computing systems may now define what it means to learn in deep networks, and how to use this knowledge for a multitude of practical applications.

Abstract: The innovation disclosed and claimed herein, in aspects thereof, comprises systems and methods of identifying AUs and emotion categories in images. The systems and methods utilized a set of images that include facial images of people. The systems and methods analyze the facial images to determine AUs and facial color due to facial blood flow variations that are indicative of an emotion category. In aspects, the analysis can include Gabor transforms to determine the AUs, AU intensities and emotion categories. In other aspects, the systems and method can include color variance analysis to determine the AUs, AU intensities and emotion categories. In further aspects, the analysis can include deep neural networks that are trained to determine the AUs, emotion categories and their intensities.

Abstract: The innovation disclosed and claimed herein, in aspects thereof, comprises systems and methods of identifying AUs and emotion categories in images. The systems and methods utilized a set of images that include facial images of people. The systems and methods analyze the facial images to determine AUs and facial color due to facial blood flow variations that are indicative of an emotion category. In aspects, the analysis can include Gabor transforms to determine the AUs. AU intensities and emotion categories. In other aspects, the systems and method can include color variance analysis to determine the AUs, AU intensities and emotion categories. In further aspects, the analysis can include deep neural networks that are trained to determine the AUs, emotion categories and their intensities.

Abstract: The innovation describes and discloses systems and methods related to deep neural networks employing machine learning to detect item 2D landmark points from a single image, such as those of an image of a face, and to estimate their 3D coordinates and shape rapidly and accurately. The system also provides for mapping by a feed-forward neural network that defines two criteria, one to learn to detect important shape landmark points on the image and another to recover their depth information. An aspect of the innovation may utilize camera models in a data augmentation approach that aids machine learning of a complex, non-linear mapping function. Other augmentation approaches are also considered.

Abstract: The innovation describes and discloses systems and methods related to deep neural networks employing machine learning to detect item 2D landmark points from a single image, such as those of an image of a face, and to estimate their 3D coordinates and shape rapidly and accurately. The system also provides for mapping by a feed-forward neural network that defines two criteria, one to learn to detect important shape landmark points on the image and another to recover their depth information. An aspect of the innovation may utilize camera models in a data augmentation approach that aids machine learning of a complex, non-linear mapping function. Other augmentation approaches are also considered.