In an illustrative embodiment, systems and methods for assisting users in identifying unknown individuals in photographs first apply facial recognition to obtain a first likelihood of match between a target face and other faces in a corpus of images provided by users of a genealogy platform, and then adjusts the first likelihood of match according to similarities and dissimilarities in attributes supplied by users regarding individuals represented by each face. Resultant likelihoods drive presentation of potential matches for consideration by a requesting user.

The present disclosure relates to a method for recognizing facial expressions based on adversarial elimination. First, a facial expression recognition network is built based on a deep convolutional neural network. On a natural facial expression data set, the facial expression recognition network is trained through a loss function to make facial expression features easier to distinguish. Then some key features of input images are actively eliminated by using an improved confrontation elimination method to generate a new data set to train new networks with different weight distributions and feature extraction capabilities, forcing the network to perform expression classification discrimination based on more features, which reduces the influence of interference factors such as occlusion on the network recognition accuracy rate, and improving the robustness of the facial expression recognition network. Finally, the final expression classification predicted results are obtained by using network integration and a relative majority voting method.

A security inspection system and a method of configuring a security inspection device are provided. In an embodiment, the security inspection system may include: an identity information entry device configured to enter an identification of an inspected person; a parameter determination device configured to determine a parameter for performing a security inspection on the inspected person based on a security factor of the inspected person determined according to user data corresponding to the identification of the inspected person; and a security inspection device configured to perform the security inspection on the inspected person based on the determined parameter. According to embodiments, it is possible to accurately predict the user's behavior and evaluate the risk or potential danger from the user by analyzing and mining the user's comprehensive data, and thus to provide a more accurate security inspection solution.

Example implementations are directed to methods and systems for individualized multimedia navigation and control including receiving metadata for a piece of digital content, where the metadata comprises a primary image and text that is used to describes the digital content; analyzing the primary image to detect one or more objects; selecting one or more secondary images corresponding to each detected object; and generating a data structure for the digital content comprising the one or more secondary images, where the digital content is described by a preferred secondary image.

Technologies are described for reconstructing physical objects which are preserved or represented in pictorial records. The reconstructed models can be three-dimensional (3D) point clouds and can be compared to existing physical models and/or other reconstructed models based on physical geometry. The 3D point cloud models can be encoded into one or more latent space feature vector representations which can allow both local and global geometric properties of the object to be described. The one or more feature vector representations of the object can be used individually or in combination with other descriptors for retrieval and classification tasks. Neural networks can be used in the encoding of the one or more feature vector representations.

A video generation method includes: obtaining a target face image and a source face image; extracting a feature of each of the source face image and the target face image through a face feature encoder, to obtain corresponding source feature codes and target feature codes; generating swapped face feature codes through a face feature exchanger according to the source feature codes and the target feature codes; generating an initial swapped face image through a face generator according to the swapped face feature codes; and fusing the initial swapped face image with the target face image through a face fuser, to obtain a final swapped face image. The face feature encoder performs hierarchical encoding on the face feature to reserve semantic details of a face, and the face feature exchanger performs further processing based on the hierarchical encoding, to obtain hierarchical encoding of a swapped face feature with semantic details.

Embodiments generally relate to systems, methods, and processes that may use touch-free document reading at self-service interaction stations. Some embodiments relate to a self service station for conducting a passenger interaction process in transit environment, including, a display screen to display a visual prompt to present a travel document in a field of view of a video image recording device as part of the passenger interaction process, and configured to determine from the received live video images a document face image present on the travel document, to determine from the received live video images a machine-readable zone (MRZ) of the travel document and store a captured MRZ image of the MRZ, to process the captured MRZ image to determine identification information on the travel document, and store the document face image and the identification information for use in the passenger interaction process.

The present disclosure relates to a video highlight extraction method and system, and a storage medium. The method includes: obtaining a to-be-processed online class video and a teacher-student interaction feature and dividing the to-be-processed online class video into a plurality of target videos; respectively analysis on pictures corresponding to all frames of a target video, to obtain a visual feature set of a student and a visual feature set of a teacher in the pictures corresponding to the frames; determining timeliness of student feedback; performing speech recognition on the speech segment corresponding to the student and the speech segment corresponding to the teacher and extracting a key word, to determine fluency of language of the teacher, fluency of language of the student, and correctness of teaching knowledge; and determining a highlight in the to-be-processed online class video according to priorities of the target videos.

The present disclosure relates to methods and devices for wireless communication of an apparatus, e.g., a UE. In one aspect, the apparatus may determine whether a connection of a video call is interrupted, the video call including a plurality of decoded frames. The apparatus may also determine, if the connection of the video call is interrupted, whether one or more decoded frames of the plurality of decoded frames are suitable for artificial frame generation. The apparatus may also generate one or more artificial frames based on the one or more decoded frames and an audio feed from a transmitting device. Additionally, the apparatus may determine whether the one or more artificial frames are suitable for a facial model call. The apparatus may also establish a facial model call based on a combination of the one or more artificial frames and the audio feed from the transmitting device.

Provided is a method for labeling a segment of a video, in a server. In the method, a multimedia file corresponding to an acting role is obtained. A role feature of the acting role is determined based on the multimedia file. A target video is decoded to obtain a data frame and a playing timestamp corresponding to the data frame, the data frame including at least one of a video frame and an audio frame. In the data frame of the target video, a target data frame that matches the role feature is identified. A segment related to performance of the acting role in the target video is automatically labeled based on a playing timestamp of the target data frame.