• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.267-268
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• 2006

Advanced TV makes the life more convenient for the viewers and it is based on the recognition technology. In this paper, we propose the implementation of face tracking system for efficient face recognition in intelligent digital TV. To recognize the face, face detection should be performed earlier. We use the motion information to track the face. Continuous face tracking is possible by using continuous detected face region and motion information. Thus the computational complexity of the recognition module in the whole system can be reduced.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.134-138
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• 2002

The human faces do not have distinct features unlike other general objects. In general the features of eyes, nose and mouth which are first recognized when human being see the face are defined. These features have different characteristics depending on different human face. In this paper, We propose a face recognition algorithm using the hidden Markov model(HMM). In the preprocessing stage, we find edges of a face using the locally adaptive threshold scheme and extract features based on generic knowledge of a face, then construct a database with extracted features. In training stage, we generate HMM parameters for each person by using the forward-backward algorithm. In the recognition stage, we apply probability values calculated by the HMM to input data. Then the input face is recognized by the euclidean distance of face feature vector and the cross-correlation between the input image and the database image. Computer simulation shows that the proposed HMM algorithm gives higher recognition rate compared with conventional face recognition algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.1041-1049
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• 2000

A face recognition algorithm for the user identification procedure of automatic teller machine(ATM), as an application of the still image processing techniques is proposed in this paper. In the proposed algorithm, face recognition techniques, especially, face region detection, eye and mouth detection schemes, which can distinguish abnormal faces from normal faces, are proposed. We define normal face, which is acceptable, as a face without sunglasses or a mask, and abnormal face, which is non-acceptable, as that wearing both, or either one of them. The proposed face recognition algorithm is composed of three stages: the face region detection stage, the preprocessing stage for facial feature detection and the eye and mouth detection stage. Experimental results show that the proposed algorithm can distinguish abnormal faces from normal faces accurately from restrictive sample images.

• Journal of Information Processing Systems
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• v.11 no.4
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• pp.643-654
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• 2015

Face recognition under controlled settings, such as limited viewpoint and illumination change, can achieve good performance nowadays. However, real world application for face recognition is still challenging. In this paper, we propose using the combination of Affine Scale Invariant Feature Transform (SIFT) and Probabilistic Similarity for face recognition under a large viewpoint change. Affine SIFT is an extension of SIFT algorithm to detect affine invariant local descriptors. Affine SIFT generates a series of different viewpoints using affine transformation. In this way, it allows for a viewpoint difference between the gallery face and probe face. However, the human face is not planar as it contains significant 3D depth. Affine SIFT does not work well for significant change in pose. To complement this, we combined it with probabilistic similarity, which gets the log likelihood between the probe and gallery face based on sum of squared difference (SSD) distribution in an offline learning process. Our experiment results show that our framework achieves impressive better recognition accuracy than other algorithms compared on the FERET database.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.977-984
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• 2007

A realtime face tracking algorithm using motion analysis from image sequences and color information is proposed. Motion area from the realtime moving images is detected by calculating temporal derivatives first, candidate pixels which represent face region is extracted by the fusion filtering with multiple color models, and realtime face tracking is performed by discriminating face components which includes eyes and lips. We improve the stability of face tracking performance by using template matching with face region in an image sequence and the reference template of face components.

• Smart Media Journal
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• v.7 no.4
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• pp.61-69
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• 2018

Multi-tracking of general objects and specific faces is an important topic in the field of computer vision applicable to many branches of industry such as biometrics, security, etc. The rapid development of deep neural networks has resulted in a dramatic improvement in face recognition and object detection problems, which helps improve the multiple-face tracking techniques exploiting the tracking-by-detection method. Our proposed method uses face detection trained with a head dataset to resolve the face deformation problem in the tracking process. Further, we use robust face features extracted from the deep face recognition network to match the tracklets with tracking faces using Hungarian matching method. We achieved promising results regarding the usage of deep face features and head detection in a face tracking benchmark.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.85-92
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• 2021

In a face, there is much information of person's identity. Because of this property, various tasks such as expression recognition, identity recognition and deepfake have been actively conducted. Most of them use the exact frontal view of the given face. However, various directions of the face can be observed rather than the exact frontal image in real situation. The profile (side view) lacks information when comparing with the frontal view image. Therefore, if we can generate the frontal face from other directions, we can obtain more information on the given face. In this paper, we propose a combined style model based the conditional generative adversarial network (cGAN) for generating the frontal face from multi-view images that consist of characteristics that not only includes the style around the face (hair and beard) but also detailed areas (eye, nose, and mouth).

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.294-301
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• 2022

COVID-19 is a crisis with numerous casualties. The World Health Organization (WHO) has declared the use of masks as an essential safety measure during the COVID-19 pandemic. Therefore, whether or not to wear a mask is an important issue when entering and exiting public places and institutions. However, this makes face recognition a very difficult task because certain parts of the face are hidden. As a result, face identification and identity verification in the access system became difficult. In this paper, we propose a system that can detect masked face using transfer learning of Yolov5s and recognize the user using transfer learning of Facenet. Transfer learning preforms by changing the learning rate, epoch, and batch size, their results are evaluated, and the best model is selected as representative model. It has been confirmed that the proposed model is good at detecting masked face and masked face recognition.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.10
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• pp.102-112
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• 1995

Temperature distribution on the rake face and flank face in orthogonal turning with cutting tool of high speed steel is studied by using a finite element method and experiments. Experiments are carried out to verify the validity of the temperature measurement by using a thermoelectric couple junction imbedded in a cutting tool of high speed steel. Good agreement is obtained between the analytical results and the experimental ones for the temperature distributions on both the rake face and flank face of cutting tool with high speed steel. The analytical results show that the temperature on the top flank face of a tool is higher than it on the top rake face of the tool because of the difference of the friction velocity on each face of the tool.

• Journal of the Korean Society of Costume
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• v.57 no.5 s.114
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• pp.151-165
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• 2007

Appearance matters in society today. Women want to feel good and look their best. They do make-up, wear garment and accessory for their good looking. Doing make-up, we have to know how we are look and to consider face shape. But it is difficult to recognize face shape. Because there is no standard face shape of adult women of quantitative analysis. The purpose of this study was to offer standard face shape of adult women in Korea. Furthermore, the study was to determine and differentiate face shape of each age group to set the basic data for the Korean beauty industry. In this study, photographs of 600 Korean women, age between $20{\sim}50's$, were indirectly measured in Venus face2D program. The measurements were analyzed by statistical methods. As a result of basic statistical data analysis, the average lengths of face were 196mm, lengths of forehead-hairline between eyebrows were 62mm, lengths of eyebrow between noses were 68mm, length of nose between chin were 66mm, and width of face were 150mm. By comparing to each age group's face using ANOVA, the statistically noticeable differences were found in measurements.