Abstract: Disclosed embodiments provide for deep convolutional neural network computing. The convolutional computing is accomplished using a multilayered analysis engine. The multilayered analysis engine includes a deep learning network using a convolutional neural network (CNN). The multilayered analysis engine is used to analyze multiple images in a supervised or unsupervised learning process. Multiple images are provided to the multilayered analysis engine, and the multilayered analysis engine is trained with those images. A subject image is then evaluated by the multilayered analysis engine. The evaluation is accomplished by analyzing pixels within the subject image to identify a facial portion and identifying a facial expression based on the facial portion. The results of the evaluation are output. The multilayered analysis engine is retrained using a second plurality of images.

Abstract: Techniques for performing viewing verification using a plurality of classifiers are disclosed. Images of an individual may be obtained concurrently with an electronic display presenting one or more images. Image classifiers for facial and head pose analysis may be obtained. The images of the individual may be analyzed to identify a face of the individual in one of the plurality of images. A viewing verification metric may be calculated using the image classifiers and a verified viewing duration of the screen images by the individual may be calculated based on the plurality of images and the analyzing. Viewing verification can involve determining whether the individual is in front of the screen, facing the screen, and gazing at the screen. A viewing verification metric can be generated in order to determine a level of interest of the individual in particular media and images.

Abstract: A machine learning system is accessed. The machine learning system is used to translate content into a representative icon. The machine learning system is used to manipulate emoji. The machine learning system is used to process an image of an individual. The machine learning processing includes identifying a face of the individual. The machine learning processing includes classifying the face to determine facial content using a plurality of image classifiers. The classifying includes generating confidence values for a plurality of action units for the face. The facial content is translated into a representative icon. The translating the facial content includes summing the confidence values for the plurality of action units. The representative icon comprises an emoji. A set of emoji can be imported. The representative icon is selected from the set of emoji. The emoji selection is based on emotion content analysis of the face.

Abstract: Generally discussed herein are devices, systems, and methods for. A method can include obtaining a normalizing autoencoder, the normalizing autoencoder trained based on first data samples of a template person and second data samples of a variety of people, normalizing, by the normalizing autoencoder, an input data sample by combining dynamic characteristics of a person in the input data sample with static characteristics in the first data samples, to generate normalized data, and providing the normalized data as input to a classifier model to classify the input data based on the dynamic characteristics of the input data and the static characteristics of the first data samples.

Abstract: Images are analyzed using sub-sectional component evaluation in order to augment classifier usage. An image of an individual is obtained. The face of the individual is identified, and regions within the face are determined. The individual is evaluated to be within a sub-sectional component of a population based on a demographic or based on an activity. An evaluation of content of the face is performed based on the individual being within a sub-sectional component of a population. The sub-sectional component of a population is used for disambiguating among content types for the content of the face. A Bayesian framework that includes a conditional probability is used to perform the evaluation of the content of the face, and the evaluation is further based on a prior event that occurred.

Abstract: In illustrative implementations of this invention, a photoplethysmographic device measures variations of light that is reflected from, or transmitted through, human skin. In some implementations, the device includes a camera that takes the measurements remotely. In others, the device touches the skin during the measurements. The device includes a camera or other light sensor, which includes at least orange, green and cyan color channels. In some cases, such as a contact device, the device includes three or more colors of active light sources, including at least orange, green and cyan light sources. A computer analyzes the sensor data, in order to estimate a cardiac blood volume pulse wave. For each cardiac pulse, a computer detects the systolic peak and diastolic inflection of the wave, by calculating a second derivative of the wave. From the estimated wave, a computer estimates heart rate, heart rate variability and respiration rate.

Abstract: Disclosed embodiments provide for deep convolutional neural network computing. The convolutional computing is accomplished using a multilayered analysis engine. The multilayered analysis engine includes a deep learning network using a convolutional neural network (CNN). The multilayered analysis engine is used to analyze multiple images in a supervised or unsupervised learning process. Multiple images are provided to the multilayered analysis engine, and the multilayered analysis engine is trained with those images. A subject image is then evaluated by the multilayered analysis engine. The evaluation is accomplished by analyzing pixels within the subject image to identify a facial portion and identifying a facial expression based on the facial portion. The results of the evaluation are output. The multilayered analysis engine is retrained using a second plurality of images.

Abstract: A machine learning system is accessed. The machine learning system is used to translate content into a representative icon. The machine learning system is used to manipulate emoji. The machine learning system is used to process an image of an individual. The machine learning processing includes identifying a face of the individual. The machine learning processing includes classifying the face to determine facial content using a plurality of image classifiers. The classifying includes generating confidence values for a plurality of action units for the face. The facial content is translated into a representative icon. The translating the facial content includes summing the confidence values for the plurality of action units. The representative icon comprises an emoji. A set of emoji can be imported. The representative icon is selected from the set of emoji. The emoji selection is based on emotion content analysis of the face.

Abstract: Techniques for performing robotic assistance are disclosed. A plurality of images of an individual is obtained by an imagery module associated with an autonomous mobile robot. Cognitive state data including facial data for the individual in the plurality of images is identified by an analysis module associated with the autonomous mobile robot. A facial expression metric, based on the facial data for the individual in the plurality of images, is calculated. A cognitive state metric for the individual is generated by the analysis module based on the cognitive state data. The autonomous mobile robot initiates one or more responses based on the cognitive state metric. The one or more responses include one or more electromechanical responses. The one or more electromechanical responses cause the robot to change locations.

Abstract: Techniques for performing viewing verification using a plurality of classifiers are disclosed. Images of an individual may be obtained concurrently with an electronic display presenting one or more images. Image classifiers for facial and head pose analysis may be obtained. The images of the individual may be analyzed to identify a face of the individual in one of the plurality of images. A viewing verification metric may be calculated using the image classifiers and a verified viewing duration of the screen images by the individual may be calculated based on the plurality of images and the analyzing. Viewing verification can involve determining whether the individual is in front of the screen, facing the screen, and gazing at the screen. A viewing verification metric can be generated in order to determine a level of interest of the individual in particular media and images.

Abstract: Concepts for facial tracking with classifiers are disclosed. A plurality of images is captured, received, and partitioned into a series of image frames. The plurality of images is captured on an individual viewing a display. One or more faces is identified and tracked in the image frames using a plurality of classifiers. The plurality of classifiers is used to perform head pose estimation. The plurality of images is analyzed to evaluate a query of determining whether the electronic display was attended by the individual with the face. The analyzing includes determining whether the individual is in front of the screen, facing the screen, and gazing at the screen. An engagement score and emotional responses are determined for media and images provided on the display. A result is rendered for the query, based on the analysis.

Abstract: In illustrative implementations of this invention, a photoplethysmographic device measures variations of light that is reflected from, or transmitted through, human skin. In some implementations, the device includes a camera that takes the measurements remotely. In others, the device touches the skin during the measurements. The device includes a camera or other light sensor, which includes at least orange, green and cyan color channels. In some cases, such as a contact device, the device includes three or more colors of active light sources, including at least orange, green and cyan light sources. A computer analyzes the sensor data, in order to estimate a cardiac blood volume pulse wave. For each cardiac pulse, a computer detects the systolic peak and diastolic inflection of the wave, by calculating a second derivative of the wave. From the estimated wave, a computer estimates heart rate, heart rate variability and respiration rate.

Abstract: In illustrative implementations of this invention, a photoplethysmographic device measures variations of light that is reflected from, or transmitted through, human skin. In some implementations, the device includes a camera that takes the measurements remotely. In others, the device touches the skin during the measurements. The device includes a camera or other light sensor, which includes at least orange, green and cyan color channels. In some cases, such as a contact device, the device includes three or more colors of active light sources, including at least orange, green and cyan light sources. A computer analyzes the sensor data, in order to estimate a cardiac blood volume pulse wave. For each cardiac pulse, a computer detects the systolic peak and diastolic inflection of the wave, by calculating a second derivative of the wave. From the estimated wave, a computer estimates heart rate, heart rate variability and respiration rate.

Abstract: Disclosed embodiments provide for deep convolutional computing image analysis. The convolutional computing is accomplished using a multilayered analysis engine. The multilayered analysis engine includes a deep learning network using a convolutional neural network (CNN). The multilayered analysis engine is used to analyze multiple images in a supervised or unsupervised learning process. The multilayered engine is provided multiple images, and the multilayered analysis engine is trained with those images. A subject image is then evaluated by the multilayered analysis engine by analyzing pixels within the subject image to identify a facial portion and identifying a facial expression based on the facial portion. Mental states are inferred using the deep convolutional computer multilayered analysis engine based on the facial expression.

Abstract: Images are analyzed using sub-sectional component evaluation in order to augment classifier usage. An image of an individual is obtained. The face of the individual is identified, and regions within the face are determined. The individual is evaluated to be within a sub-sectional component of a population based on a demographic or based on an activity. An evaluation of content of the face is performed based on the individual being within a sub-sectional component of a population. The sub-sectional component of a population is used for disambiguating among content types for the content of the face. A Bayesian framework that includes a conditional probability is used to perform the evaluation of the content of the face, and the evaluation is further based on a prior event that occurred.

Abstract: Image content is analyzed in order to present an associated representation expression. Images of one or more individuals are obtained and the processors are used to identify the faces of the one or more individuals in the images. Facial features are extracted from the identified faces and facial landmark detection is performed. Classifiers are used to map the facial landmarks to various emotional content. The identified facial landmarks are translated into a representative icon, where the translation is based on classifiers. A set of emoji can be imported and the representative icon is selected from the set of emoji. The emoji selection is based on emotion content analysis of the face. The selected emoji can be static, animated, or cartoon representations of emotion. The individuals can share the selected emoji through insertion into email, texts, and social sharing websites.

Abstract: Facial expressions are evaluated for control of robots. One or more images of a face are captured. The images are analyzed for mental state data. The images are analyzed to determine a facial expression of the face within an identified a region of interest. Mental state information is generated. A context for the robot operation is determined. A context for the individual is determined. The actions of a robot are then controlled based on the facial expressions and the mental state information that was generated. Displays, color, sound, motion, and voice response for the robot are controlled based on the facial expressions of one or more people.

Abstract: Mental state event signatures are used to assess how members of a specific social group react to various stimuli such as video advertisements. The likelihood that a video will go viral is computed based on mental state event signatures. Automated facial expression analysis is utilized to determine an emotional response curve for viewers of a video. The emotional response curve is used to derive a virality probability index for the video. The virality probability index is an indicator of the propensity to go viral for a given video. The emotional response curves are processed according to various demographic criteria in order to account for cultural differences amongst various demographic groups and geographic regions.

Abstract: Facial evaluation is performed on one or more videos captured from an individual viewing a display. The images are evaluated to determine whether the display was viewed by the individual. The individual views a media presentation that includes incorporated tags and is rendered on the display. Based on the tags, video of the individual is captured and evaluated using a classifier. The evaluating includes determining whether the individual is in front of the screen, facing the screen, and gazing at the screen. An engagement score and emotional responses are determined for media and images provided on the display.

Abstract: A sensor system includes one or more gyroscopes and one or more accelerometers, for measuring subtle motions of a user's body. The system estimates physiological parameters of a user, such as heart rate, breathing rate and heart rate variability. When making the estimates, different weights are assigned to data from different sensors. For at least one estimate, weight assigned to data from at least one gyroscope is different than weight assigned to data from at least one accelerometer. Also, for at least one estimate, a weight assigned to one or more sensors located in a first region relative to the user's body is different than a weight assigned to one or more sensors located in a second region relative to the user's body. Furthermore, weight assigned to data from at least one sensor changes over time.