Abstract: A method, medium, and apparatus with a generated object descriptor using a curvature gabor filter, with complementary object recognition being performed by applying a general gabor filter and a curvature gabor filter having various curvatures, such that the dimension and efficiency of a feature vector can be increased by detecting correlations between features of an object searching for a parameter and its position of an optimal curvature gabor filter through boosting learning, and projecting the extracted features onto a base vector having the highest classification rate.

Abstract: A yeast extract or yeast-derived bioactive peptide having an activity of selectively inhibiting reuptake of serotonin and norepinephrine, which can be effectively used in preventing or treating various diseases related to reuptake of serotonin and norepinephrine, especially depression, anxiety, stress, fatigue and obesity is provided. Also, a yeast extract or yeast-derived bioactive peptide having activities as an anti-stress agent, an anti-fatigue agent, premenstrual syndrome (PMS) and menstrual pain relaxants, and a brain-neurotrophic factor, and a method for preparing the bioactive peptide is provided. The yeast extract or yeast-derived bioactive peptide is effective in relieving stress, nervousness, anxiety, tension, insomnia, fatigue, and imbalance in the autonomic nerve regulation.

Abstract: Provided are a method and apparatus for extracting facial features from an image containing a face. The method and apparatus filter an input image using a filter set for face recognition at each of predetermined locations in the input image, merge values obtained by filtering the input image at locations, which are horizontally symmetrical to each other with respect to the center of the face, and synthesize values obtained by filtering the input image at locations which are not symmetrical to each other with respect to the center of the face with the merged values. Therefore, the time, feature values, and storage space required to extract or compare facial features can be significantly reduced. In addition, a face recognition system that runs well on low specification hardware can be implemented.

Abstract: A face descriptor generating method and apparatus and face recognition method and apparatus using extended local binary pattern (LBP) are provided. Since LBP features are selected by performing a supervised learning process on the extended LBP features and the selected extended LBP features are used in face recognition, it is possible to reduce errors in face recognition or identity verification and to increase face recognition efficiency. In addition, the extended LBP features are used so that it is possible to overcome the problem of time-consumption of the process.

Abstract: 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.

Abstract: A method and apparatus for calculating a similarity of a face image, a method and apparatus for retrieving a face image by using the calculation method and apparatus, and a method of synthesizing a face image are provided. According to the methods and apparatuses, by separately calculating a holistic similarity of a face image based on a holistic feature of the face image, and a local similarity of a face image based on local features of the face image, and adding the calculated similarity results according to weights, thereby calculating the similarity between compared face images, the similarity calculation result of the face considering both the holistic feature and local features of the face image can be obtained, thereby improving reliability of the face image similarity calculation and lowering complexity of the calculation.

Abstract: A face recognition method and apparatus using extended Gabor wavelet features are provided. In the face recognition method, extended Gabor wavelet features are extracted from a face image by applying an extended Gabor wavelet filter, a Gabor wavelet feature set is selected by performing a supervised learning process on the extended Gabor wavelet features, and the selected Gabor wavelet feature set is used for face recognition. Accordingly, it is possible to solve problems of a high error rate of face recognition and low face recognition efficiency caused from a limitation of parameters of the Gabor wavelet filter. In addition, it is possible to solve the problem of increased calculation complexity caused from using an extended Gabor wavelet filter and to implement robust face recognition which is excellent in dealing with a change in expression and illumination.

Abstract: A video data processing system including a clustering unit to generate a plurality of clusters by grouping a plurality of shots forming video data, the grouping being based on a similarity between the plurality of shots, and a final cluster determiner to identify a cluster having the greatest number of shots from the plurality of clusters to be a first cluster and determining a final cluster by comparing other clusters with the first cluster.

Abstract: 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.

Abstract: 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.

Abstract: A scalable face recognition method and apparatus using complementary features. The scalable face recognition apparatus includes a multi-analysis unit which analyzes a plurality of features of an input face image using a plurality of feature analysis techniques separately, compares the features of the input face image with a plurality of features of a reference image; and provides similarities as the results of the comparison, a fusion unit which fuses the similarities, and a determination unit which classifies the input face image according to a result of the fusion performed by the fusion unit.

Abstract: Methods, mediums and systems recognizing a face by extracting features from a facial image. According to the method recognizing the face, multiple subimages of a query facial image and one or more target facial images are generated, Fourier transforms on the multiple subimages are performed, and Fourier features from the multiple subimages are extracted using the Fourier-transformed multiple subimages. A similarity between the Fourier features of the query facial image and the one or more target facial images is measured, and similarities with respect to a plurality of target facial images are calculated. An image having a maximum similarity to the query facial image from the one or more target facial images is selected.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.