Disclosed is an augmented reality system to generate and cause display of an augmented reality interface at a client device. Various embodiments may detect speech, identify a source of the speech, transcribe the speech to a text string, generate a speech bubble based on properties of the speech and that includes a presentation of the text string, and cause display of the speech bubble at a location in the augmented reality interface based on the source of the speech.

A method, apparatus, and non-transitory computer readable medium for image processing are described. Embodiments of the method, apparatus, and non-transitory computer readable medium include identifying an original image including a plurality of semantic attributes, wherein each of the semantic attributes represents a complex set of features of the original image; identifying a target attribute value that indicates a change to a target attribute of the semantic attributes; computing a modified feature vector based on the target attribute value, wherein the modified feature vector incorporates the change to the target attribute while holding at least one preserved attribute of the semantic attributes substantially unchanged; and generating a modified image based on the modified feature vector, wherein the modified image includes the change to the target attribute and retains the at least one preserved attribute from the original image.

A control method for a human-computer interaction device, a human-computer interaction device, and a human-computer interaction system are described. The control method includes: capturing first voice information of a first object; identifying a second object related to the first voice information; acquiring first information related to the second object; and presenting the first information.

Methods and apparatuses for detecting a face, and electronic devices include: performing face location on a face image to be detected; performing face attribute detection on the face image based on a face location result; and displaying a face attribute detection result of the face image to be detected. Use experience of face image detection can be improved while diversified requirements of a user for obtaining corresponding face information in a face image from different angles are satisfied.

Techniques for reconstructing three-dimensional face images are described herein. The disclosed techniques include acquiring a real two-dimensional face key point and a predicted two-dimensional face key point; solving a first loss function consisting of the real two-dimensional face key point, the predicted two-dimensional face key point and a preset additional regular constraint term to iteratively optimize an expression coefficient, where the additional regular constraint term is used for constraining the expression coefficient such that the expression coefficient represents a real state of a face; and reconstructing a three-dimensional face image based on the expression coefficient.

An expression recognition method under a natural scene comprises: converting an input video into a video frame sequence in terms of a specified frame rate, and performing facial expression labeling on the video frame sequence to obtain a video frame labeled sequence; removing natural light impact, non-face areas, and head posture impact elimination on facial expression from the video frame labeled sequence to obtain an expression video frame sequence; augmenting the expression video frame sequence to obtain a video preprocessed frame sequence; from the video preprocessed frame sequence, extracting HOG features that characterize facial appearance and shape features, extracting second-order features that describe a face creasing degree, and extracting facial pixel-level deep neural network features by using a deep neural network; then, performing vector fusion on these three obtain facial feature fusion vectors for training; and inputting the facial feature fusion vectors into a support vector machine for expression classification.

Systems and methods for using message data analysis for response recommendations are disclosed. For example, personalized emojis may be generated utilizing one or more image-capture techniques and/or analysis of image data depicting a user. Those personalized emojis may be saved to a library of personalized emojis may be utilized to respond to received messages. Analysis of received message may also be performed, and the results of the analysis may be utilized to recommend automatic responses, including previously-generated personalized emojis.

An information processing apparatus in the present invention includes: an acquisition unit that acquires first history information indicating that a procedure related to boarding of a passenger in an airport was performed with biometric authentication and second history information indicating that the procedure was performed with reading of a medium; and an output unit that outputs usage status of the biometric authentication in the procedure based on the first history information and the second history information.

Estimating emotion may include obtaining an image of at least part of a face, and applying, to the image, an expression convolutional neural network (“CNN”) to obtain a latent vector for the image, where the expression CNN is trained from a plurality of pairs each comprising a facial image and a 3D mesh representation corresponding to the facial image. Estimating emotion may further include comparing the latent vector for the image to a plurality of previously processed latent vectors associated with known emotion types to estimate an emotion type for the image.

A system and method for determining a mood for a crowd is disclosed. In example embodiments, a method includes identifying an event that includes two or more attendees, receiving at least one indicator representing emotions of attendees, determining a numerical value for each of the indicators, and aggregating the numerical values to determine an aggregate mood of the attendees of the event.