Aac Optics Solutions Pte. Ltd.

Tencent Technology (shenzhen) Company Limited

Motorola Mobility LLC

Shenzhen Goodix Technology Co., Ltd.

Raytech Optical (changzhou) Co., Ltd.

Shuang Wu

Lei Zhang

Yanmei Wang

Amit Kumar Agrawal

Hiroyuki Teraoka

Jilin Li

Gautham Prabhakar Natakala

Shouhong Ding

Takaaki Teranishi

Teranishi Takaaki

Dan Liu

Bo Li

Bin Wu

Chao Chen

Jun Fang

Yao Liu

Shaoxin Li

Huafei Li

Pengcheng Shen

Taiping Yao

Yicong Liang

Jia Meng

Oinuma Kenji

Wenhe Jin

Yufeng Liu

Anping Li

Bangjie Yin

Jianghe Xu

An image processing method, including: obtaining an original image, the original image including biological features of a to-be-detected object, fusing a global frequency domain map corresponding to the original image with the original image to obtain a global fusion image, performing living body detection on the global fusion image to obtain a first detection result, determining that the original image does not pass the living body detection when the first detection result indicates that the original image belongs to a screen reproduced image, obtaining, when the original image does not belong to the screen reproduced image, a biological feature image based on the biological features, fusing a local frequency domain map with the biological feature image to obtain a local fusion image, performing living body detection on the local fusion image to obtain a second detection result, and determining a living body detection result corresponding to the original image.

Image processing method and apparatus, device, storage medium, and computer program product

An image attack detection method includes: acquiring an image-to-be-detected, and performing global classification recognition based on the image-to-be-detected to obtain a global classification recognition result; performing local image extraction randomly based on the image-to-be-detected to obtain a target number of local images, the target number being obtained by calculation according to a defensive rate of a reference image corresponding to the image-to-be-detected; performing local classification recognition based on the target number of local images respectively to obtain respective local classification recognition results, and fusing the respective local classification recognition results to obtain a target classification recognition result; and detecting a similarity between the target classification recognition result and the global recognition result, and determining the image-to-be-detected as an attack image when the target classification recognition result and the global classification recognition result are dissimilar.

Image attack detection method and apparatus, and image attack detection model training method and apparatus

In aspects of personal content managed during extended display screen recording, a screen recording system includes a wireless device that provides digital image content for display on an extended display device, and a screen recording session on the wireless device captures the digital image content and audio data. The wireless device implements a content control module that can determine the screen recording session would capture personal content associated with a user of the wireless device. The content control module can initiate a private screen review mode in which the personal content is displayable on a display screen of the wireless device and is prevented from visual display on the extended display device. The content control module can also generate a shareable screen recording that includes the audio data and the digital image content displayed on the extended display device, without including the personal content.

Personal content managed during extended display screen recording

A camera optical lens is provided. The camera optical lens includes, from an object side to an image side, a first lens, a second lens having a positive refractive power, a third lens, a fourth lens, a fifth lens, a sixth lens, a seventh lens, an eighth lens, and a ninth lens. The camera optical lens satisfies following conditions: 3.50≤f1/f≤6.00, and 1.50≤d15/d16≤9.00, where f denotes a focal length of the camera optical lens, f1 denotes a focal length of the first lens is defined as f1, d15 denotes an on-axis thickness of the eighth lens, and d16 denotes an on-axis distance from an image side surface of the eighth lens to an object side surface of the ninth lens. The camera optical lens has a good optical performance, and meets design requirements of a large aperture, wide-angle and ultra-thinness.

Camera optical lens

This application relates to a model training method. The method includes retrieving a current group of training samples, the training samples being based on a training set; obtaining first sample features of training samples in the current group of training samples based on a to-be-trained model; and obtaining, center features respectively corresponding to the training samples; obtaining feature distribution parameters corresponding to the training samples, the feature distribution parameter corresponding to each training sample being obtained by collecting statistics on second sample features of training samples in the training set that belong to the same classification category, and the second sample feature of each training sample being generated by a trained model; obtaining, based on the center features and the feature distribution parameters, a comprehensive loss parameter corresponding to the current group of training samples; and adjusting model parameters of the to-be-trained model based on the comprehensive loss parameter.

Model training method, storage medium, and computer device

The present disclosure relates to the field of optical lenses and provides a camera optical lens. The camera optical lens includes, from an object side to an image side: a first lens having a positive refractive power; a second lens having a positive refractive power; a third lens having a positive refractive power; a fourth lens; a fifth lens; and a sixth lens. The camera optical lens satisfies following conditions: 2.00&#x2264;f2/f3&#x2264;5.00; and 1.00&#x2264;d3/d5&#x2264;2.00. The camera optical lens can achieve a high imaging performance while obtaining a low TTL.

The present disclosure relates to the field of optical lenses and provides a camera optical lens. The camera optical lens includes, from an object side to an image side: a first lens; a second lens having a positive refractive power; a third lens having a positive refractive power; a fourth lens; a fifth lens; and a sixth lens. The camera optical lens satisfies following conditions: 1.40&#x2264;f1/f3&#x2264;5.00; and 2.50&#x2264;R5/R6&#x2264;6.00. The camera optical lens can achieve a high imaging performance while obtaining a low TTL.

Camera optical lens including six lenses of +++&#x2212;+&#x2212; refractive powers

The present disclosure relates to the field of optical lenses and provides a camera optical lens. The camera optical lens includes, from an object side to an image side: a first lens; a second lens having a positive refractive power; a third lens having a positive refractive power; a fourth lens; a fifth lens; and a sixth lens. The camera optical lens satisfies following conditions: 3.50&#x2264;f1/f3&#x2264;7.00; and &#x2212;10.00&#x2264;R5/R6&#x2264;&#x2212;5.00. The camera optical lens can achieve a high imaging performance while obtaining a low TTL.

The present disclosure relates to the field of optical lenses and provides a camera optical lens. The camera optical lens includes, from an object side to an image side: a first lens having a positive refractive power; a second lens having a positive refractive power; a third lens having a positive refractive power; a fourth lens; a fifth lens; and a sixth lens. The camera optical lens satisfies following conditions: 5.00&#x2264;f2/f3&#x2264;8.00; and 1.00&#x2264;d3/d5&#x2264;5.00. The camera optical lens can achieve a high imaging performance while obtaining a low TTL.

A face liveness recognition method includes: obtaining a target image containing a facial image; extracting facial feature data of the facial image in the target image; performing face liveness recognition according to the facial feature data to obtain a first confidence level using a first recognition model, the first confidence level denoting a first probability of recognizing a live face; extracting background feature data from an extended facial image, the extended facial image being obtained by extending a region that covers the facial image; performing face liveness recognition according to the background feature data to obtain a second confidence level using a second recognition model, the second confidence level denoting a second probability of recognizing a live face; and according to the first confidence level and the second confidence level, obtaining a recognition result indicating that the target image is a live facial image.

Living body recognition method, storage medium, and computer device

A data live streaming method is provided. The data live streaming method includes: receiving, by at least one processor, frame data from clients in a client set, wherein each client in the client set is synchronously running a target application; broadcasting, by the at least one processor, the frame data to each client in the client set to perform data exchange; and synchronously sending, by the at least one processor, the frame data to a mobile terminal, wherein the mobile terminal generates live streaming data corresponding to the target application according to the frame data and prestored data corresponding to the target application.

Data live streaming method, and related device and system

The present disclosure discloses a camera optical lens. The camera optical lens including, in an order from an object side to an image side, a first lens, a second lens having a positive refractive power, a third lens having a positive refractive power, a fourth lens, a fifth lens, and a sixth lens. The camera optical lens further satisfies specific conditions.

Growing community of inventors

Shenzhen, China

Shuang Wu