Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and a method for synthesizing a realistic image with a new expression of a face in an input image by receiving an input image comprising a face having a first expression; obtaining a target expression for the face; and extracting a texture of the face and a shape of the face. The program and method for generating, based on the extracted texture of the face, a target texture corresponding to the obtained target expression using a first machine learning technique; generating, based on the extracted shape of the face, a target shape corresponding to the obtained target expression using a second machine learning technique; and combining the generated target texture and generated target shape into an output image comprising the face having a second expression corresponding to the obtained target expression.

Provided is an information processing apparatus that includes an acquisition unit and a generation control unit. The acquisition unit acquires input information including at least one of an image or audio of a first user. The generation control unit controls, on the basis of request information including a request to output information including at least one of an image or audio and the acquired input information, generation of output information related to the first user to be output by a terminal of a second user who is a communication partner of the first user.

Aspects of the technology described herein improve an object recognition system by specifying a type of picture that would improve the accuracy of the object recognition system if used to retrain the object recognition system. The technology described herein can take the form of an improvement model that improves an object recognition model by suggesting the types of training images that would improve the object recognition model's performance For example, the improvement model could suggest that a picture of a person smiling be used to retrain the object recognition system. Once trained, the improvement model can be used to estimate a performance score for an image recognition model given the set characteristics of a set of training of images. The improvement model can then select a feature of an image, which if added to the training set, would cause a meaningful increase in the recognition system's performance

This application discloses methods, apparatus, and electronic devices for face pose estimation and three-dimensional face reconstruction. The face pose estimation method comprises: acquiring a two-dimensional face image for processing, constructing a three-dimensional face model corresponding to the two-dimensional face image, and determining a face pose of the two-dimensional face image based on face feature points of the three-dimensional face model and face feature points of the two-dimensional face image. With this approach, the face pose estimation is performed based on the three-dimensional face model corresponding to the two-dimensional face image, instead of only based on a three-dimensional average face model. As a result, a high accuracy pose estimation can be obtained even for a face with large-angle and exaggerated facial expressions. Thus, robustness of the pose estimation can be effectively improved.

A mobile device can implement a neural network-based style transfer scheme to modify an image in a first style to a second style. The style transfer scheme can be configured to detect an object in the image, apply an effect to the image, and blend the image using color space adjustments and blending schemes to generate a realistic result image. The style transfer scheme can further be configured to efficiently execute on the constrained device by removing operational layers based on resources available on the mobile device.

Disclosed embodiments provide for vehicle manipulation with convolutional image processing. The convolutional image processing uses a multilayered analysis engine. A plurality of images is obtained using an imaging device within a first vehicle. A multilayered analysis engine is trained using the plurality of images. The multilayered analysis engine includes multiple layers that include convolutional layers and hidden layers. The evaluating provides a cognitive state analysis. Further images are evaluated using the multilayered analysis engine. The further images include facial image data from one or more persons present in a second vehicle. Manipulation data is provided to the second vehicle based on the evaluating the further images. An additional plurality of images of one or more occupants of one or more additional vehicles is obtained. The additional images provide opted-in, crowdsourced image training. The crowdsourced image training enables retraining the multilayered analysis engine.

Techniques are disclosed for training and applying nonlinear face models. In embodiments, a nonlinear face model includes an identity encoder, an expression encoder, and a decoder. The identity encoder takes as input a representation of a facial identity, such as a neutral face mesh minus a reference mesh, and outputs a code associated with the facial identity. The expression encoder takes as input a representation of a target expression, such as a set of blendweight values, and outputs a code associated with the target expression. The codes associated with the facial identity and the facial expression can be concatenated and input into the decoder, which outputs a representation of a face having the facial identity and expression. The representation of the face can include vertex displacements for deforming the reference mesh.

Rendering an avatar in a selected environment may include determining as inputs into an inferred shading network, an expression geometry to be represented by an avatar, head pose, and camera angle, along with a lighting representation for the selected environment. The inferred shading network may then generate a texture of a face to be utilized in rendering the avatar. The lighting representation may be obtained as lighting latent variables which are obtained from an environment autoencoder trained on environment images with various lighting conditions.

The present disclosure relates to system(s) and method(s) for generating an automatic virtual photo album. The system receives a signal. The signal is configured to enable a rear camera and a front camera of a device to click a set of images. The set of images comprise a subset of front images and a subset of rear images. The subset of front images is further analysed. Based on the analysis, the system extracts a photographer mood. The system further links the photographer mood with the subset of rear images. Upon the linking, the system generates an automatic photo album.

System, method, and computer program product to implement four factor authentication rule-based intelligent bank cards, by receiving valid authentication information for a card associated with an account, capturing an image of a person presenting the card, applying an algorithm to the image to determine that the image capture was successful, and the image can be used by a facial recognition algorithm, verifying that the image matches an image of an authorized user of the account, analyzing image data of the image with a facial emotion recognition algorithm to detect an emotion expressed by the person in the image, identifying an emotion rule associated with the account and the emotion, providing, by the processor, an alert to a person associated with the account, wherein the alert is specified as part of the emotion rule, and restricting access to the account.