Samsung Electronics Co., Ltd.

Nec Corporation

Seok-cheol Kee

Tae-kyun Kim

Hyun-woo Kim

Won-Jun Hwang

Jong-ha Lee

Du-Sik Park

Chang-yeong Kim

Haitao Wang

Hyun-hwa Oh

Chang-kyu Choi

Toshio Kamei

Jung-Bae Kim

Young-shin Kwak

Haibing Ren

Jiali Zhao

Sang-min Yoon

Provided are an object verification apparatus and method. The object verification apparatus includes a matching unit performing a plurality of different matching algorithms on a query image and generating a plurality of scores; a score normalization unit normalizing each of the generated scores to be adaptive to the query image; a weight estimation unit estimating weights of the normalized scores based on the respective matching algorithms applied; and a score fusion unit fusing the normalized scores by respectively applying the weights estimated by the weight estimation unit to the normalized scores.

Object verification apparatus and method

A method and apparatus for performing a conversion of a skin color of an input image into a preference color by applying face detection and skin color detection is disclosed. The method includes: detecting a face area from the input image; detecting a skin area from the input image; judging a common area between the face area and the skin area as a face; extracting a skin color from the input image with reference to the skin color in the judged face; and converting the extracted skin color into an image-adaptive skin color.

Method and apparatus for performing conversion of skin color into preference color by applying face detection and skin area detection

A method and apparatus for extracting feature vectors for face recognition and retrieval are provided. An entire Fourier feature vector is generated for an entire face area of a normalized face image using first and second normalized vectors, and a central Fourier feature vector is generated for a central face area of the normalized face image using third and fourth normalized vectors. An entire intensity feature vector is generated for the entire face area, and a local intensity feature vector is generated for a predetermined number of face component-wise areas. An entire composite feature vector is generated by coupling the first and second normalized vectors and the entire intensity feature, and a central composite feature vector is generated by coupling the third and fourth normalized vectors and the local intensity feature vectors.

Method and apparatus for extracting feature vector used for face recognition and retrieval

The present invention relates to a system and method for detecting a face that is capable of quickly and correctly deciding whether an input facial image is occluded, regardless of any type of facial image to be inputted. The present invention is characterized in that eigenvectors and weights are extracted from the input facial image using principal component analysis (PCA) and the extracted eigenvectors and weights of the user image are substituted into a occluding-decision algorithm, whereby it can be determined whether the facial image is occluded.

System and method for detecting face

An image retrieval method and apparatus using iterative matching are provided. The method comprises: (a) extracting K similar images in order of matching rank by retrieving N reference images stored in an image database through comparison between a query image selected by a user and the reference images; and (b) performing iterative matching for M similar images, which are ranked higher and selected from the K similar images, with a predetermined frequency, and rearranging the matching ranks of the M similar images. According to the method and apparatus, among similar images retrieved by using a query image selected by a user, M similar images that are ranked higher are selected, and by performing iterative matching for the M similar images using the high-ranked similar image, M similar images are rearranged in order of similarity and output. Accordingly, accuracy of the matching can improve greatly and a load to the retrieval engine due to iterative retrieval can be minimized.

Image retrieval method and apparatus using iterative matching

A face recognition method using artificial neural network and an apparatus thereof are provided. The apparatus comprises an eigenpaxel selection unit which generates eigenpaxels indicating characteristic patterns of a face and selects a predetermined number of eigenpaxels among the generated eigenpaxels; an eigenfiltering unit which filters an input image with the selected eigenpaxels; a predetermined number of neural networks, each of which corresponds to one of the selected eigenpaxels, receives an image signal which is filtered by the corresponding eigenpaxel, and output a face recognition result; and a determination unit which receives the recognition result from each of the neural networks and outputs a final face recognition result of the input image.

Face recognition method using artificial neural network and apparatus thereof

A method and apparatus for recognizing and searching for a face using 2nd-order independent component analysis (ICA) are provided. The method for describing feature points uses 2nd-order ICA d to describe a facial image space and improve recognition performance in various illumination conditions. According to the method and apparatus, use of pose or illumination invariant face descriptor enables retrieval of human faces and authentication of a specific individual.

Method and apparatus of recognizing face using component-based 2nd-order principal component analysis (PCA)/independent component analysis (ICA)

Provided are a face recognition apparatus and method in which a facial image is divided into facial component images. The apparatus includes a component division unit that divides an input facial image into a plurality of facial component images; a face descriptor generating unit that generates face descriptors using a transform matrix corresponding to the respective facial component images, the face descriptors being characteristic vectors; a registered face descriptor database (DB) that stores registered face descriptors; and an authentication unit that authenticates the input facial image by comparing face descriptors for the input facial image input from the face descriptor generating unit with the registered face descriptors and providing predetermined weights corresponding to each facial component to the comparison results of each facial component.

Face recognition method and apparatus using component-based face descriptor

To reduce degradation of recognition performance due to eye detection errors during face verification and to overcome a problem in that sufficient data to design an optimum feature classifier cannot be obtained during face registration, a method includes shifting the positions of eyes detected during face registration in predetermined directions by a predetermined distance to generate pairs of new coordinate points of the eyes; normalizing a face image on the basis of each pair of new coordinate points of the eyes; using the results of normalization in teaching a feature classifier, thereby coping with eye detection errors. In addition, two threshold values are used to prevent a database from being updated with a face of an unregistered person and to update the database with a normal client's face image that has been used during the latest face verification.

Method for verifying users and updating database, and face verification system using the same

A method and apparatus for recognizing and searching for a face using 2nd-order independent component analysis (ICA) are provided. The method includes performing PCA on an input original image and generating a low-pass filtered image by removing high-frequency component from the original image, subtracting the low-pass filtered image from the original image and obtaining residual image having only frequency-components, and performing ICA on the residual image and generating feature information corresponding to the original image. While an original ICA subtracts independent components (i.e., independent basis components) from gray scale images, the proposed 2nd-order ICA is robust to illumination variation since it is applied to make the remaining images correspond to high pass frequency components.

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Kyungki-do, South Korea

Seok-cheol Kee