Electronics and Telecommunications Research Institute

Samsung Electronics Co., Ltd.

Woo-Han Yun

Hyun-Soo Kim

Je-Han Yoon

Do-Hyung Kim

Young-Hee Park

Jae-Yeon Lee

Min-Su Jang

Hyun-Sik Shim

Su-Young Chi

Ji-Hyo Lee

Mi-Young Cho

Soo-Young Chi

Disclosed herein is a method for controlling a serving robot based on meal context. The method includes receiving table information from a table robot corresponding to each table in a service area and controlling driving of a serving robot using the meal context and a navigation map, and the meal context includes information about the seating positions of customers and meal status information corresponding to the table.

Method and apparatus for controlling serving robot based on information from table

A face recognition system based on adaptive learning includes a specific person detection and tracking unit for detecting and tracking a specific person from a moving image. A facial feature extraction unit extracts a plurality of facial feature vectors from the detected and tracked specific person. A face recognition unit searches for a given registration model by comparing the extracted facial feature vectors with facial feature vectors of the registration models previously stored in a user registration model database. A learning target selection unit selects a facial feature vector to be added to a record of the given registration model from among the extracted facial feature vectors. A registration model learning unit adds and updates the selected facial feature vector to the record of the given registration model.

Face recognition system and method based on adaptive learning

A system and method for verifying the face of a user using a light mask are provided. The system includes a facial feature extraction unit for extracting a facial feature vector from a facial image received from a camera. A non-user Gaussian Mixture Model (GMM) configuration unit generates a non-user GMM from a facial image stored in a non-user database (DB). A user GMM configuration unit generates a user GMM by applying light masks to a facial image stored in a user DB. A log-likelihood value calculation unit inputs the facial feature vector both to the non-user GMM and to the user GMM, thus calculating log-likelihood values. A user verification unit compares the calculated log-likelihood values with a predetermined threshold, thus verifying whether the received facial image is a facial image of the user.

System and method for verifying face of user using light mask

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Daejeon, South Korea

Woo-Han Yun