Electronics and Telecommunications Research Institute

Ho Sub Yoon

Jae Yeon Lee

Jae Hong Kim

Hye Jin Kim

Joo Chan Sohn

Su Young Chi

Woo Han Yun

Jung Soh

Do-Hyung Kim

Kye Kyung Kim

Kyu Seo Han

Byung Tae Chun

Young Woo Yoon

Kyu Dae Ban

Se Yoon Jeong

Yun Koo Chung

Dong Jin Lee

Young Lae Bae

Do Hyung Kim

Ji Eun Kim

Jong-Hyun Park

Jae Il Cho

Young-Jo Cho

Chan Kyu Park

Dae Ha Lee

Myung Ae Chung

Mun Sung Han

Yongwha Chung

Young Kyu Yang

Suyoung Chi

Kyu-Dae Ban

Byung Woo Min

Youngwoo Yoon

Jae Yoon Jang

The present invention relates to a method and apparatus for re-registering pre-registered identity information in a new identity recognition system. A method of re-registering identity feature information in an identity recognition system according to an embodiment of the present disclosure may include: identifying one or more first identity feature information calculated by the first feature calculation device and stored in a database; generating one or more latent space information by inputting the one or more first identity feature information into a specific model for latent space estimation; generating one or more second identity feature information by inputting the one or more latent space information into a second feature calculation device; and storing the one or more second identity feature information in a new database.

Method and apparatus for re-registering pre-registered identity information in new identity recognition system

An apparatus includes an image receiving module configured to collect a depth image provided from a camera, a human body detection module configured to detect a human body from the collected depth image, and an activity recognition module configured to recognize an action of the human body on the basis of a 3-dimensional action volume extracted from the human body and a previously learned action model.

Apparatus and method for providing activity recognition based application service

Provided is a gesture recognition apparatus. The gesture recognition apparatus includes a human detection unit, a gesture region setting region, an arm detection unit and a gesture determination unit. The human detection unit detects a face region of a user from an input image. The gesture region setting unit sets a gesture region, in which a gesture of the user's arm occurs, with respect to the detected face region. The arm detection unit detects an arm region of the user in the gesture region. The gesture determination unit analyzes a position, moving directionality and shape information of the arm region in the gesture region to determine a target gesture of the user. Such a gesture recognition apparatus may be used as a useful means for a human-robot interaction in a long distance where a robot has difficulty in recognizing a user's voice.

Gesture recognition apparatus, robot system including the same and gesture recognition method using the same

A method and apparatus for proving sign information are disclosed. The sign information providing method includes: extracting a first sign from an input image, wherein the first sign is pre-defined; extracting a second sign representing information corresponding to the first sign around the location of the first sign, from the input image; and providing at least one piece of information of information about the first sign and information about the second sign in the form of voice. Accordingly, a user may correctly recognize information expressed by a sign.

Method and apparatus for providing sign information

An apparatus for discriminating a disguised face includes a face area detector configured to detect a face area in an input image provided from an external source. The apparatus includes a skin color modeling module configured to separate a skin color area from the face area and a disguised face discriminator configured to determine whether signals in the skin color area have a pulse component to discriminate whether a face in the input image is the disguised face.

Apparatus and method for discriminating disguised face

Disclosed is a method of sequencing character information in order to increase precision of character recognition. The method includes: a pre-processing that extracts character information from an image to binarize the extracted character information through a predetermined threshold and extracts and thins a center line of the binarized character information; normalizing the pre-processed character information to character information according to a predetermined criteria; and sequencing the normalized character information using structural features including an end point or a divergence point of the character information. The present invention suggests an angle normalization method of input character information, a structural feature position determining method, and a structural feature numeral string generating method to strongly recognize characters configured by various fonts obtained from a natural scene regardless of an angle or a size of the characters.

Method and apparatus for sequencing off-line character from natural scene

Disclosed are a system and a method for recognizing a disguised face using a Gabor feature and a support vector machine (SVM) classifier according to the present invention. The system for recognizing a disguised face includes: a graph generation means to generate a single standard face graph from a plurality of facial image samples; a support vector machine (SVM) learning means to determine an optimal classification plane for discriminating a disguised face from the plurality of facial image samples and disguised facial image samples; and a facial recognition means to determine whether an input facial image is disguised using the standard face graph and the optimal classification plane when the facial image to be recognized is input.

System for recognizing disguised face using gabor feature and SVM classifier and method thereof

Disclosed is a method of detecting an upper body. The method includes detecting an omega candidate area including a shape formed of a face and a shoulder line of a human from a target image, cutting the target image into the upper body candidate area including the omega candidate area, detecting a human face from the upper body candidate area, and judging whether the upper body of the human is included in the target image according to the result of detecting the human face.

Apparatus and method for detecting upper body

The present invention detects a candidate ROI group associated with character strings/figure strings on the basis of a result acquired through prior learning of various types of license plates, verifies the interested region candidate group detected by using at least one condition of five predetermined conditions, and determines an MBR region in the selected ROI region from the verified interested region candidate group by considering a ratio between the height and width of the ROI region to recognize the license plate for the automobile. According to the present invention, it is possible to automatically detect the location of the license plate regardless of various types of license plate specifications defined for each of countries.

Apparatus and method for recognizing identifier of vehicle

A gesture spotting detection method and apparatus employ a shoulder-line algorithm. The shoulder-line detecting method recognizes a GSD calling gesture that occurs in a shoulder-line, head or higher part in a remote distance or a short distance, although a user does not have a fixed posture. In the method, an image of people is received, and skin information of a person in the image is detected to detect a face area. Then, the cloth color information of the person is modeled from the inputted image to detect a cloth area. An external space is defined from the image based on the body space area, and an edge is extracted from the image based on the body space and the external space. Then, shoulder-line information is acquired based on an energy function obtained based on the body space, the external space, and the edge.

Method and apparatus for shoulder-line detection and gesture spotting detection

A method and apparatus for recognizing a gesture in an image processing system. In the apparatus, an input unit receives an image obtained by capturing a gesture of a user using a camera. A detector detects a face area in the input image, and detects a hand area in gesture search areas. The gesture search areas being set by dividing the image into predetermined areas with reference to a predetermined location of the detected face area. A controller sets the gesture search areas, determines whether a gesture occurs in the detected hand area, and selects a detection area with respect to the gesture to generate a control command for controlling an image device. A calculator calculates skin-color information and differential-area information for checking a gesture in the detected hand area. Accordingly, a hand area can be accurately detected, and a gesture can be separated from peripheral movement information, so that mal-functioning caused by gesture recognition can be reduced.

Method and apparatus for recognizing gesture in image processing system

In an apparatus for online signature verification, a signature data input unit digitalizes a locus of a user signature and reads the locus as a sequence of points. A first and a second pattern transform unit performs a speed equalization and a velocity transform on the signature sequence and generates a first and a second transformed pattern sequence, respectively. A characteristics extraction unit extracts three characteristics vectors from the signature sequence, the first and the second transformed pattern sequence, respectively, to thereby generate the three characteristics vectors. A difference vector estimation unit generates a difference vector. A determination unit determines whether an input signature and the reference signature are signed by a single person.

Apparatus for online signature verification using pattern transform technique and method therefor

A face detecting system using a symmetric axis includes: an image inputting unit for inputting a still image thereto; an image converting unit for reducing the inputted image to a first certain size of image for a real-time process by using a pyramidal resolution reduction way; a scanning unit for scanning the reduced image by applying a second certain size of symmetric mask; a region dividing unit for dividing a region of the scanned mask into a left region and a right region with respect to the symmetric axis; a symmetric-similarity computing unit for computing a symmetric similarity of a local region at the two divided regions and then computing a symmetric similarity of a global region to compute a symmetric similarity of a face region; and a face region extracting unit for extracting a region having a symmetric axis at which the computed symmetric similarity is above a predetermined value, as the face region.

Face detecting system and method using symmetric axis

A camera information coding/decoding method for synthesizing stereoscopic real video and a computer graphic image is disclosed. The method stores the camera information generated when picturing, which is not contained in the existing multimedia contents coding standard, that is, the location motion information of the camera and the camera viewpoint displacement information, together with contents as the camera motion information stream. The camera information obtained through the camera position sensor and the camera viewpoint displacement sensor when picturing image is coded and stored together with the contents, thereby easily and precisely synthesizing with the computer graphic image. In addition, since the information may be easily transformed to the descriptor of the camera motion information of MPEG-7 format, there is another advantage of easily carrying out camera motion information indexing operation among searching methods of the multimedia contents.

Camera information coding/decoding method for synthesizing stereoscopic real video and a computer graphic image

A method and an apparatus for estimating a camera motion include a frame picture memory unit for storing a plurality of moving picture frames, a buffer for sequentially extracting the frames and storing them, a block division unit for dividing each of the frames stored in the buffer into a plurality of blocks, a mean picture generation unit for generating horizontal and vertical mean pictures using the blocks, a horizontal and a vertical mean picture memory unit for storing the data of the horizontal and vertical mean pictures, a motion vector extraction unit for extracting motion vectors from the blocks using horizontal and vertical mean picture data, a motion vector memory unit for storing the data of the motion vectors, and a camera motion estimation unit for estimating a camera motion using the motion vector data.

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Daejeon, South Korea

Ho Sub Yoon