Beijing Kuangshi Technology Co., Ltd.

Pinhole (beijing) Technology Co., Ltd.

Megvii (beijing) Technology Co., Ltd.

Microsoft Technology Licensing, LLC

Hefei Xinsheng Optoelectronics Technology Co., Ltd.

Boe Technology Group Co., Ltd.

Beijing Boe Technology Development Co., Ltd.

Qi Yin

Gang Yu

Haoqiang Fan

Chao Li

Qizheng He

Zhimin Cao

Yuning Jiang

Kai Jia

Muge Chen

Erjin Zhou

Yuxiang Peng

Jian Qiao Sun

Xiaoou Tang

Shuchang Zhou

Qingchao Meng

Xinyu Zhou

Cong Yao

Tongsheng Fan

Jian Xu

Li Li Ma

Zhongqi Zhang

Jiahui Yu

Yichuan Tan

Xiaosuo Ma

Weiran He

Dieqiao Feng

Le Lin

Yisu Zhou

Zongyi Chen

Yuxiang Ma

A touch panel and a manufacturing method thereof, and a display device are provided. The touch panel includes a base substrate, the base substrate includes a touch area and a lead end area; the touch area is provided with a plurality of first electrodes and a plurality of second electrodes, and the second electrodes are intersected with and insulated from the first electrodes; a plurality of leads, electrically connected with the plurality of first electrodes and the plurality of second electrodes respectively, and connected to the lead end area (), and the plurality of leads include a transparent conductive layer lead.

Touch panel and manufacturing method thereof, display device

A flexible touch panel includes a flexible substrate including a flexible substrate body and at least one flexible substrate extension, in which the flexible substrate body is contiguous with the at least one flexible substrate extension. A plurality of touch units are provided on the flexible substrate body. A plurality of signal lines are disposed on the flexible substrate and electrically connected to the plurality of touch units. Each of the plurality of signal lines includes a first portion on the flexible substrate body and a second portion on the flexible substrate extension, so that each of the plurality of signal lines directly connects to an external driving circuit, and some of the plurality of signal lines include a material different from others of the plurality of signal lines.

Flexible touch panel comprising a flexible substrate with a fan-out region, manufacturing method thereof and flexible touch display

The application provides a liveness detection method and a liveness detection system. The liveness detection method includes: obtaining first and second face image data of an object to be detected, and at least one of the first and the second face image data being a depth image; determining a first face region and a second face region, determining whether the first and the second face regions correspond to each other, and extracting, when it is determined that the first and the second face region corresponds to each other, a first and a second face image from the first and the second face region respectively; determining a first classification result for the extracted first face image and a second classification result for the extracted second face image; and determining, based on the first classification result and the second classification result, a detection result for the object to be detected.

Liveness detection method and liveness detection system

The embodiments of the present invention provide training and construction methods and apparatus of a neural network for object detection, an object detection method and apparatus based on a neural network and a neural network. The training method of the neural network for object detection, comprises: inputting a training image including a training object to the neural network to obtain a predicted bounding box of the training object; acquiring a first loss function according to a ratio of the intersection area to the union area of the predicted bounding box and a true bounding box, the true bounding box being a bounding box of the training object marked in advance in the training image; and adjusting parameters of the neural network by utilizing at least the first loss function to train the neural network.

Method and apparatus for neural network training and construction and method and apparatus for object detection

An information processing method comprises: acquiring an original webpage; parsing the original webpage to determine a set of candidate images; detecting whether at least one candidate image in the set of candidate images contains a face; determining a target face from at least one detected face, and determining a candidate image corresponding to the target face as a target image; analyzing the target face to obtain at least one content feature of the original webpage, wherein the at least one content feature includes at least one of a face attribute feature and a personal identity feature; determining display contents matching the at least one content feature based on the at least one content feature; inserting the display contents into a target area corresponding to the target image in the original webpage in a form of a dialog box to acquire a target webpage; and outputting the target webpage.

Information processing method and information processing apparatus

This application provides a video monitoring method and process. The video monitoring method comprises: obtaining first and second video data of a scene being monitored; detecting at least one target object based on the first video data, and determining parameter information of the at least one target object in at least one frame of the first video data, the parameter information including a first position; determining, based on coordinate transforming relationship between the first and the second video data, a second position of the at least one target object in a corresponding frame in the second video data according to the first position; and extracting, based on the second video data, feature information of the at least one target object located in the second position, wherein orientations with which the first and the second video acquiring module acquire video data with respect to a ground plane are different.

Video monitoring method and video monitoring system

The application provides a liveness detection method capable of implementing liveness detection on a human body, and a liveness detection system that employs the liveness detection method. The liveness detection method comprises: obtaining video data acquired via a video acquisition module; determining, based on the video data, a feature signal of an object to be detected; judging whether the feature signal meets a first predetermined condition, and if the feature signal meets the first predetermined condition, identifying that the object to be detected is a living body, wherein the feature signal is indicative of eye movement of the object to be detected.

Liveness detection method, liveness detection system, and liveness detection device

This application provides a video monitoring method and device. The video monitoring method includes: obtaining video data; inputting at least one frame in the video data into a first neural network to determine object amount information of each pixel dot in the at least one frame; and executing at least one of the following operations by using a second neural network: performing a smoothing operation based on the object amount information in the at least one frame to rectify the object amount information; determining object density information of each pixel dot in the at least one frame based on scene information and the object amount information; predicting object density information of each pixel dot in a to-be-predicted frame next to the at least one frame based on the scene information, the object amount information, and association information between the at least one frame and the to-be-predicted frame.

Video monitoring method and video monitoring device

The application provides a liveness detection method capable of implementing liveness detection, and a liveness detection system that employs the liveness detection method. The liveness detection method comprises: irradiating an object to be detected with structured light; obtaining first facial image data of the object to be detected under irradiation of the structured light; determining, based on the first facial image data, a detection parameter that indicates a sub-surface scattering intensity of the structured light on a face of the object to be detected; and determining, based on the detection parameter and a predetermined parameter threshold, whether the object to be detected is a living body.

Liveness detection method, liveness detection system, and computer program product

Provided are an image processing method and a client device, an image authentication method and a server device. The image processing method comprises: photographing an object to obtain a first image; sending a key acquiring request to a server and receiving a key from the server; generating a second image based on the key and the first image; and sending the second image to the server. The image authentication method comprises: receiving a key acquiring request from a client; generating a key and sending the key to the sever in response to the key acquiring request; receiving an image for authentication from the client; and authenticating the image for authentication by using the key.

Image processing method and client device, image authentication method and server device

There are provided a liveness detection method and device. The liveness detection method comprises: generating a random action instruction sequence including at least one random action instruction; sequentially sending a random action instruction in the random action instruction sequence; and determining whether the sequentially sent random action instruction in the random action instruction sequence is sequentially executed by a living body based on detection information of at least two sensors, wherein the at least two sensors comprise an image sensor and at least one non-image sensor; and determining that the liveness detection is succeeded if the sequentially sent random action instruction in the random action instruction sequence is sequentially executed by the living body. Accuracy of liveness detection can be improved by adopting the random action sequence and by combining images captured by the image sensor and information detected by the non-image sensor.

Method and apparatus for liveness detection

There are provided a focusing point determining method and apparatus. The focusing point determining method comprises: obtaining a view-finding image within a view-finding coverage; identifying a significance area in the view-finding image; and extracting at least one focusing point from the identified significance area. By identifying the significance area in the view-finding image and extracting at least one focusing point from the identified significance area, the focusing point determining method and apparatus can ensure accuracy of a selected focusing point to a certain extent, so as to ensure accuracy of focusing.

Focusing point determining method and apparatus

A method and an apparatus for measuring a speed of an object are provided, the method comprising: acquiring, for each image frame of at least two image frames in an image sequence, image information of the image frame, wherein the image information comprises depth information; detecting at least one object in the image frame based on the depth information to obtain an object detection result for the image frame; tracking the at least one detected object based on the object detection result of each of the at least two image frames to obtain an object tracking result; and calculating the speed of the at least one detected object based on the object tracking result and a time difference between the at least two image frames.

Method and apparatus for detecting a speed of an object

A disclosed face detection system (and method) is based on a structure of a convolutional neural network (CNN). One aspect concerns a method for automatically training a CNN for face detection. The training is performed such that balanced number of face images and non-face images are used for training by deriving additional face images from the face images. The training is also performed by adaptively changing a number of trainings of a stage according to automatic stopping criteria. Another aspect concerns a system for performing image detection by integrating data at different scales (i.e., different image extents) for better use of data in each scale. The system may include CNNs automatically trained using the method disclosed herein.

Face detection using machine learning

Provided are a driving assistance information generating method and device, and a driving assistance system, which relate to a field of vehicle driving assistance technique. The driving assistance information generating method comprises: obtaining a depth image acquired by a depth camera and a scene image acquired by an imaging camera, the depth camera and the imaging camera being mounted on a vehicle in a manner of being registered and matched with each other, and a coverage of the depth camera and a coverage of the imaging camera being at least partially overlapped; detecting positions of respective objects appearing in the scene image by using the depth image and the scene image; and generating driving assistance information of the vehicle based on positions of the respective objects.

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Beijing, China

Qi Yin