One embodiment provides a method comprising receiving a first input image and a second input image. Each input image comprises a facial image of an individual. For each input image, a first set of facial regions of the facial image is distinguished from a second set of facial regions of the facial image based on a learning based model. The first set of facial regions comprises age-invariant facial features, and the second set of facial regions comprises age-sensitive facial features. The method further comprises determining whether the first input image and the second input images comprise facial images of the same individual by performing face verification based on the first set of facial regions of each input image.

In some aspects, a server receives a video stream from a security system and processes a frame from the video stream to create a processed frame. The server analyzes the processed frame using artificial intelligence and determines that the processed frame includes a change to a surface area of an object and determines details associated with the change. The server determines that the change satisfies one or more thresholds, such as a change threshold and a time threshold. The server adds annotations to the processed frame to create an annotated frame. The annotations include the change and at least a portion of the details associated with the change to the surface area of the object. The server sends, to a designated recipient, a notification that includes a link to view the annotated frame.

Photographic emoji communications systems and methods of use are provided herein. An example method receiving a plurality of image files from a user device, each of the image files including a selfie of the user; for each of the plurality of image files, determining a reaction emotion of an associated selfie based on facial attributes of the user; storing the plurality of image files in a repository, each of the plurality of image files being labeled with a respective reaction emotion as a selfiemoji; receiving a request to include one of the selfiemojis in a message; and inserting one of the selfiemojis into the message.

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.

Provided are systems and methods for face reenactment. An example method includes receiving visual data including a visible portion of a source face, determining, based on the visible portion of the source face, a first portion of source face parameters associated with a parametric face model, where the first portion corresponds to the visible portion, predicting, based partially on the visible portion of the source face, a second portion of the source face parameters, where the second portion corresponds to the rest of the source face, receiving a target video that includes a target face, determining, based on the target video, target face parameters associated with the parametric face model and corresponding to the target face, and synthesizing, using the parametric face model, based on the source face parameters and the target face parameters, an output face that includes the source face imitating a facial expression of the target face.

Facial expressions are recognized using relations determined by class-to-class comparisons. In one example, descriptors are determined for each of a plurality of facial expression classes. Pair-wise facial expression class-to-class tasks are defined. A set of discriminative image patches are learned for each task using labelled training images. Each image patch is a portion of an image. Differences in the learned image patches in each training image are determined for each task. A relation graph is defined for each image for each task using the differences. A final descriptor is determined for each image by stacking and concatenating the relation graphs for each task. Finally, the final descriptors of the images of the are fed into a training algorithm to learn a final facial expression model.

A method for pushing information based on a user emotion including recordings of behavior habits of the user based on a number of predefined emotions within a predefined time period can be implemented in the disclosed electronic device. Based on each predefined emotion, a proportion of each behavior habit of the user is determined at the predetermined time intervals. The device determines information to be pushed according to a current user emotion and the proportions of the behavior habits of the user corresponding to the current user emotion, and the electronic device is controlled to push the determined information.

The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing a machine learning model trained to determine subtle pose differentiations to analyze a repository of captured digital images of a particular user to automatically capture digital images portraying the user. For example, the disclosed systems can utilize a convolutional neural network to determine a pose/facial expression similarity metric between a sample digital image from a camera viewfinder stream of a client device and one or more previously captured digital images portraying the user. The disclosed systems can determine that the similarity metric satisfies a similarity threshold, and automatically capture a digital image utilizing a camera device of the client device. Thus, the disclosed systems can automatically and efficiently capture digital images, such as selfies, that accurately match previous digital images portraying a variety of unique facial expressions specific to individual users.

A system, article, and method to provide facial expression recognition using linear relationships within landmark subsets.

A software agent configured to perform operations of: receiving from a human user an image in a message within a communication event established between a user terminal associated with the human user and the software agent; transmitting image data from the image to at least three image processing service components, including: (i) first image processing service component for detecting physical characteristic of a facial image and providing raw data pertaining to physical characteristics; (ii) a second image processing service component for detecting emotional characteristics of a facial image and providing raw data pertaining to emotional characteristics; and (iii) a third image processing service component to detect whether an image is a facial image or a non-facial image and providing a probability indication; processing the raw data from the first and second image processing service and the probability indication from the third image processing service to generate humanly readable text for incorporation in a response message; transmitting the response message in the communication event to the user terminal for display to a human user at the user terminal.