Samsung Electronics Co., Ltd.

Jinwoo Son

Changyong Son

Chang Kyu Choi

Jaejoon Han

Byungin Yoo

Seungju Han

Sangil Jung

Deoksang Kim

Dae-kyu Shin

Wonsuk Chang

Jihye Kim

Minsu Ko

Jae-Joon Han

Seohyung Lee

Jaehyoung Yoo

Youngjun Kwak

Sungun Park

Changin Choi

Jungbae Kim

Dongwook Lee

Changkyo Lee

Byung In Yoo

Seungin Park

Seoh Yung Lee

San Gil Jung

An image sensor includes: a motion detection circuit configured to detect a motion in image frames; and a micro control unit (MCU) configured to adjust at least a portion of a target frame among the image frames based on whether the motion is detected, and detect whether a target object is present based on the adjusted portion of the target frame.

Image sensor and operating method of the image sensor

A neural network processing method and apparatus based on nested bit representation is provided. The processing method includes obtaining first weights for a first layer of a source model of a first layer of a neural network, determining a bit-width for the first layer of the neural network, obtaining second weights for the first layer of the neural network by extracting at least one bit corresponding to the determined bit-width from each of the first weights for the first layer of a source model corresponding to the first layer of the neural network, and processing input data of the first layer of the neural network by executing the first layer of the neural network based on the obtained second weights.

Neural network processing method and apparatus based on nested bit representation

Provided is a method and apparatus with neural network operation. The method includes generating a first intermediate operation result by performing a first-order partial operation of a neural network layer on a first input line of a first area of a frame, generating a second intermediate operation result by performing another first-order partial operation of the neural network layer on a second input line of the first area, and generating an objective operation result of the neural network layer with respect to the first area based on a second-order partial operation performed on the first intermediate operation result and the second intermediate operation result.

Method and apparatus with neural network operation

Disclosed is a face verification method and apparatus. The method including analyzing a current frame of a verification image, determining a current frame state score of the verification image indicating whether the current frame is in a state predetermined as being appropriate for verification, determining whether the current frame state score satisfies a predetermined validity condition, and selectively, based on a result of the determining of whether the current frame state score satisfies the predetermined validity condition, extracting a feature from the current frame and performing verification by comparing a determined similarity between the extracted feature and a registered feature to a set verification threshold.

Face verification method and apparatus

Disclosed is a face verification method and apparatus. A mobile device may include one or more processors configured to obtain one or more images for a user, ascertain whether any of the one or more images correspond to respective user distances, from the user to the mobile device, outside of a threshold range of distances, and selectively, based on a result of the ascertaining, perform verification using a first verification threshold for any of the one or more images ascertained to correspond to the respective user distances that are outside the threshold range of distances, and perform verification using a less strict second verification threshold for any of the one or more images that have been ascertained to not correspond to the respective user distances that are outside the threshold range of distances.

A system includes: an image sensor configured to acquire an image; an image processor configured to generate a quantized image based on the acquired image using a trained quantization filter; and an output interface configured to output the quantized image.

Method and apparatus with quantized image generation

An image matching method includes extracting, from a first image of an object, a landmark patch including a landmark point of the object, extracting, from a second image of the object, a target patch corresponding to the landmark patch; and determining a target point in the second image corresponding to the landmark point based on a matching between the landmark patch and the target patch.

Device and method with image matching

Disclosed is a processor-implemented data processing method in a neural network. A data processing apparatus includes at least one processor, and at least one memory configured to store instructions to be executed by the processor and a neural network, wherein the processor is configured to, based on the instructions, input an input activation map into a current layer included in the neural network, output an output activation map by performing a convolution operation between the input activation map and a weight quantized with a first representation bit number of the current layer, and output a quantized activation map by quantizing the output activation map with a second representation bit number based on an activation quantization parameter.

Method and apparatus with neural network parameter quantization

A convolutional neural network (CNN) processing method includes selecting a survival network in a precision convolutional network based on a result of performing a high speed convolution operation between an input and a kernel using a high speed convolutional network, and performing a precision convolution operation between the input and the kernel using the survival network.

Convolutional neural network (CNN) processing method and apparatus performing high speed and precision convolution operations

A processor-implemented neural network method includes: obtaining a first weight kernel of a weight model and pruning information of the first weight kernel; determining, based on the pruning information, a processing range of an input feature map for each weight element vector of the first weight kernel; performing a convolution operation between the input feature map and the first weight kernel based on the determined processing range; and generating an output feature map of a neural network layer based on an operation result of the convolution operation.

Method and apparatus with neural network operation processing

Disclosed are an image data processing method and a sensor device performing the same. The sensor device includes an image sensor configured to acquire image data, an image buffer configured to store the image data, and an image processor configured to generate image-processed data by applying a filter corresponding to a storage pattern of the image buffer to the image data stored in the image buffer.

Data processing method and sensor device for performing the same

A liveness test method and apparatus is disclosed. A processor implemented liveness test method includes extracting an interest region of an object from a portion of the object in an input image, performing a liveness test on the object using a neural network model-based liveness test model, the liveness test model using image information of the interest region as provided first input image information to the liveness test model and determining liveness based at least on extracted texture information from the information of the interest region by the liveness test model, and indicating a result of the liveness test.

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Seoul, South Korea

Jinwoo Son