• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.670-674
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• 1996

In automated search of a design database to support mechanical design, it is necessaryto recognize a shape pattern which represents a design object. This paper introduces the concept of a surface relation graph (SRG) for recognizing shape patterns from a 3D boundary representation scheme of a solid model(a B-rep model). In SRG, the nodes and arcs correspond to the faces and edges shared by two adjacent faces, respectively. An attribute assigned to an arc is given by an integer which discriminates the relationship between two adjacent faces. The + sign of the integer represents the geometric convexity of the solid, and the -sign the concivity at the shared edge. The input shape is recognized by comparison with the predefined features which are subgraphs of the SRG. A hierarchyof the database for upporting the design is presented. A search for the design database is also discussed. The usefulness of this method is illustrated by some application results.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2004.11a
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• pp.49-52
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• 2004

A few studies have shown that the function of fusiform face area is selectively involved in the perception of faces including a race difference. We investigated the neural substrates of the face-selective region called fusiform face area In the ventral occipital-temporal cortex and same-race memory superiority In the fusiform face area by the event-related fMRI. In our fMRI study, twelve healthy subjects (Oriental-Korean) performed the implicit distinction of the race while they consciously made familiar-judgments, regardless of whether they considered a face as Oriental-Korean or European-American. In the race distinction as an implicit task, the fusiform face areas (FFA) and the right parahippocampal gyrus had a greater response to the presentation of Oriental-Korean than European-American faces, but in the consciously race distinction between Oriental-Korean and European-American faces, any significant difference in the FFA was not observed. These results suggest that different activation in the fusiform regions and right parahippocampal gyrus resulting from same-race memory superiority could be implicitly taken place by the physiological processes of face recognition.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.2
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• pp.77-82
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• 2012

This paper presents a hardware-implemented face regions stabilization algorithm that stabilizes facial regions using the locations and sizes of human faces found by a face detection system. Face detection algorithms extract facial features or patterns determining the presence of a face from a video source and detect faces via a classifier trained on example faces. But face detection results has big variations in the detected locations and sizes of faces by slight shaking. To address this problem, the high frequency reduce filter that reduces variations in the detected face regions by taking into account the face range information between the current and previous video frames are implemented in addition to center distance comparison and zooming operations.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1105-1112
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• 2020

Gait recognition, as a promising biometric, can be used in video-based surveillance and other security systems. However, due to the complexity of leg movement and the difference of external sampling conditions, gait recognition still faces many problems to be addressed. In this paper, an improved convolutional neural network (CNN) based on Gabor filter is therefore proposed to achieve gait recognition. Firstly, a gait feature extraction layer based on Gabor filter is inserted into the traditional CNNs, which is used to extract gait features from gait silhouette images. Then, in the process of gait classification, using the output of CNN as input, we utilize metric learning techniques to calculate distance between two gaits and achieve gait classification by k-nearest neighbors classifiers. Finally, several experiments are conducted on two open-accessed gait datasets and demonstrate that our method reaches state-of-the-art performances in terms of correct recognition rate on the OULP and CASIA-B datasets.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3236-3238
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• 1999

This paper proposes a new face recognition technique in the dynamic link architecture which shows robustness against size variation and distortion. The face recognition technique in the dynamic link architecture so far was not appropriate for the recognition of various size of faces because of the fixed size of the graph and the fixed value of a of the Gabor filter not considering the size of the face. The proposed face recognition algorithm can represent the input facial image by a suitable size of labeled graph, and it can also adjust the dilation width and the height of the vibrating amplitude of the Gabor filter, thus face recognition in the dynamic link architecture is even applicable regardless of the size of the face.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.682-689
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• 2019

In part-based image representation, the partial shapes of an object are represented as basis vectors, and an image is decomposed as a linear combination of basis vectors where the coefficients of those basis vectors represent the partial (or local) feature of an object. In this paper, a face recognition for occluded faces is proposed in which face images are represented using non-negative matrix factorization(NMF), one of part-based representation techniques, and recognized using an artificial neural network technique. Standard NMF, projected gradient NMF and orthogonal NMF were used in part-based representation of face images, and their performances were compared. Learning vector quantizer were used in the recognizer where Euclidean distance was used as the distance measure. Experimental results show that proposed recognition is more robust than the conventional face recognition for the occluded faces.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.737-742
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• 1994

In this paper , we propose an incremental approach for recognizing a class of machining features from a featurebased design model as a part design proceeds, utilizing various information such as nominal geometry, design intents, and design feature characteristics. The proposed apptroach can handle complex intersecting features and protrusion features designed on oblique faces. The class of recognized volumetric machining features can be expressed as Material Removal Shape Element Volumes (MRSEVs), a PDES/STEP-based library of machining features.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.217-218
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• 2007

Face recognition, recognizing the human faces, is one of the most important techniques for making intelligent robot that provide commendable services to human. In this paper, we make a comparative study of Original PCA, 2DPCA, 2DPCA based algorithms and LDA in robot environment. Database is obtained through the robot's camera in a laboratory what is made like home environment for experiment.. We consider distance state what can be generated in home environment for database.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.402-410
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• 2017

For many years, researchers in face description area have been representing and recognizing faces based on different methods that include subspace discriminant analysis, statistical learning and non-statistics based approach etc. But still automatic face recognition remains an interesting but challenging problem. This paper presents a novel and efficient face image representation method based on Optimized Local Ternary Pattern (OLTP) texture features. The face image is divided into several regions from which the OLTP texture feature distributions are extracted and concatenated into a feature vector that can act as face descriptor. The recognition is performed using nearest neighbor classification method with Chi-square distance as a similarity measure. Extensive experimental results on Yale B, ORL and AR face databases show that OLTP consistently performs much better than other well recognized texture models for face recognition.

• 전자공학회논문지 IE
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• v.43 no.1
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• pp.25-31
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• 2006

In this paper, This study proposes a method that use the whole face as features by using a color information and KLT that overcome the weak points of existing face extraction and face recognition. The significant information among the features of face is extracted by PCA which uses KLT. In this paper, you will find that the recognition efficiency is over 90% for the faces that have various size and angle by proposing the face recognition method using color information and the KLT.