Abstract: Systems and methods for more accurate and robust determination of subject characteristics from an image of the subject. One or more machine learning models receive as input an image of a subject, and output both facial landmarks and associated confidence values. Confidence values represent the degrees to which portions of the subject's face corresponding to those landmarks are occluded, i.e., the amount of uncertainty in the position of each landmark location. These landmark points and their associated confidence values, and/or associated information, may then be input to another set of one or more machine learning models which may output any facial analysis quantity or quantities, such as the subject's gaze direction, head pose, drowsiness state, cognitive load, or distraction state.

Abstract: The invention relates to the automated determination of the staining quality of an IHC stained biological sample. A plurality of features is extracted from a digital IHC stained tissue image. The features are input into a first classifier configured to identify the extended tissue type of the depicted tissue as a function of the extracted features. An extended tissue type is a tissue type with a defined expression level of the tumor marker. In addition, the extracted features are input into a second classifier configured to identify a contrast level of the depicted tissue as a function of at least some second ones of the extracted features. The contrast level indicates the intensity contrast of pixels of the stained tissue. Then, a staining quality score of the image is computed as a function of the identified extended tissue type and the identified contrast level.

Abstract: The invention relates to the automated determination of the staining quality of an IHC stained biological sample. A plurality of features is extracted from a digital IHC stained tissue image. The features are input into a first classifier configured to identify the extended tissue type of the depicted tissue as a function of the extracted features. An extended tissue type is a tissue type with a defined expression level of the tumor marker. In addition, the extracted features are input into a second classifier configured to identify a contrast level of the depicted tissue as a function of at least some second ones of the extracted features. The contrast level indicates the intensity contrast of pixels of the stained tissue. Then, a staining quality score of the image is computed as a function of the identified extended tissue type and the identified contrast level.

Abstract: An eye gaze tracking system is disclosed. The system includes a gaze data acquisition system including a plurality of light sources and a plurality of image sensors. The plurality of light sources are arranged to emit light to a head of the user, and the plurality of image sensors are configured to receive the light. In an embodiment, the system further includes a gaze tracking module including an ocular feature extraction module, a point of regard (POR) calculation module and a POR averaging module. The ocular feature extraction module is configured to process the gaze data and to extract ocular features, and is configured to determine a confidence value associated with an accuracy of the parameters. The POR calculation module is configured to determine a POR from the ocular features. The POR averaging module is configured to determine an average POR.

Abstract: An eye gaze tracking system is disclosed. The system includes a gaze data acquisition system including a plurality of light sources and a plurality of image sensors. The plurality of light sources are arranged to emit light to a head of the user, and the plurality of image sensors are configured to receive the light. In an embodiment, the system further includes a gaze tracking module including an ocular feature extraction module, a point of regard (POR) calculation module and a POR averaging module. The ocular feature extraction module is configured to process the gaze data and to extract ocular features, and is configured to determine a confidence value associated with an accuracy of the parameters. The POR calculation module is configured to determine a POR from the ocular features. The POR averaging module is configured to determine an average POR.