• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.39-42
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• 2023

Recently, with the explosive increase of video streaming services, real-time live broadcasting has also increased, which leads to an infringement problem for user privacy. So, to solve such problems, we proposed the real image blurring system using dlib face-recognition library. 68 face landmarks are extracted and convert into 128 vector values. After that the proposed system tries to compare this value with the image in the database, and if it is over 0.45, it is considered as different person and image blurring processing is performed. With the proposed system, it is possible to solve the problem of user privacy infringement, and also to be utilized to detect the specific person. Through experimental results, the proposed system has an accuracy of more than 90% in terms of face recognition.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.2
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• pp.38-42
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• 2016

In this paper, we propose 3-factor mobile banking authentication scheme applied to face recognition techniques with existing certificate and OTP. An image of the user's face is captured by smart phone camera and its brightness processing of the contour of a face and background by n of X and Y points. Then, distance between the point of eyes, nose and mouth from captured user's face are compared with stored facial features. When the compared results corresponding to the data that stored in a face recognition DB, the user is authenticated.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.96-99
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• 2020

According to brilliant development of smart devices, many related services are being devised. And, almost every service is designed to provide user-centric services based on personal information. In this situation, to prevent unintentional leakage of personal information is essential. Conventionally, ID and Password system is used for the user authentication. This is a convenient method, but it has a vulnerability that can cause problems due to information leakage. To overcome these problem, many methods related to face recognition is being researched. Through this paper, we investigated the trend of user authentication through biometrics and a representative model for face recognition techniques. One is DeepFace of FaceBook and another is FaceNet of Google. Each model is based on the concept of Deep Learning and Distance Metric Learning, respectively. And also, they are based on Convolutional Neural Network (CNN) model. In the future, further research is needed on the equipment configuration requirements for practical applications and ways to provide actual personalized services.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.2
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• pp.106-110
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• 2014

This paper proposed the face recognition method which can be effectively applied to the robot's vision system. The proposed algorithm is recognition using hue extraction and feature point. hue extraction was using difference of skin color, pupil color, lips color. Features information were extraction from eye, nose and mouth using feature parameters of the difference between the feature point, distance ratio, angle, area. Feature parameters fuzzified data with the data generated by membership function, then evaluate the degree of similarity was the face recognition. The result of experiment are conducted with frontal color images of face as input images the received recognition rate of 96%.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.95-105
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• 2005

As the security system using facial recognition, the recognition performance depends on the environments (e. g. face expression, hair style, age and make-up etc.) For the revision of easily changeable environment, it's generally used to set up the threshold, replace the face image which covers the threshold into images already registered, and update the face images additionally. However, this usage has the weakness of inaccuracy matching results or can easily active by analogous face images. So, we propose the genetic algorithm which absorbs greatly the facial similarity degree and the recognition target variety, and has excellence studying capacity to avoid registering inaccuracy. We experimented variable and similar face images (each 30 face images per one, total 300 images) and performed inherent face images based on ingredient analysis as face recognition technique. The proposed method resulted in not only the recognition improvement of a dominant gene but also decreasing the reaction rate to a recessive gene.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4903-4929
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• 2018

In this study, a fully automatic pose and expression invariant 3D face alignment algorithm is proposed to handle frontal and profile face images which is based on a two pass course to fine alignment strategy. The first pass of the algorithm coarsely aligns the face images to an intrinsic coordinate system (ICS) through a single 3D rotation and the second pass aligns them at fine level using a minimum nose tip-scanner distance (MNSD) approach. For facial recognition, multi-view faces are synthesized to exploit real 3D information and test the efficacy of the proposed system. Due to optimal separating hyper plane (OSH), Support Vector Machine (SVM) is employed in multi-view face verification (FV) task. In addition, a multi stage unified classifier based face identification (FI) algorithm is employed which combines results from seven base classifiers, two parallel face recognition algorithms and an exponential rank combiner, all in a hierarchical manner. The performance figures of the proposed methodology are corroborated by extensive experiments performed on four benchmark datasets: GavabDB, Bosphorus, UMB-DB and FRGC v2.0. Results show mark improvement in alignment accuracy and recognition rates. Moreover, a computational complexity analysis has been carried out for the proposed algorithm which reveals its superiority in terms of computational efficiency as well.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2004-2007
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• 2003

This paper describes a method of performance enhancement using Flatness Mesure(FM) for the Gaussian Mixture Model(GMM) face recognition systems. Using this measure we discard the frames having low information before training and test. As the result, the performance increases about 9％ in the lower mixtures and calculation burden is decreased. As well, the recognition error rate is decreased under the illumination change surroundings. We use the 2D DCT coefficients lot face feature vectors and experiments are carried out on the Olivetti Research Laboratory (ORL) face database.

• Journal of IKEEE
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• v.22 no.2
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• pp.281-288
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• 2018

In this paper, we propose an intelligent face recognition and tracking system that distributes GPU resources using CUDA. The proposed system consists of five steps such as GPU allocation algorithm that distributes GPU resources in optimal state, face area detection and face recognition using deep learning, real time face tracking, and PTZ camera control. The GPU allocation algorithm that distributes multi-GPU resources optimally distributes the GPU resources flexibly according to the activation level of the GPU, unlike the method of allocating the GPU to the thread fixedly. Thus, there is a feature that enables stable and efficient use of multiple GPUs. In order to evaluate the performance of the proposed system, we compared the proposed system with the non - distributed system. As a result, the system which did not allocate the resource showed unstable operation, but the proposed system showed stable resource utilization because it was operated stably. Thus, the utility of the proposed system has been demonstrated.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.255-263
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• 2006

In the last decade, face analysis, e.g. face detection, face recognition, facial expression recognition, is a very lively and expanding research field. As computer animated agents and robots bring a social dimension to human computer interaction, interest in this research field is increasing rapidly. In this paper, we introduce an artificial emotion mimic system which can recognize human facial expressions and also generate the recognized facial expression. In order to recognize human facial expression in real-time, we propose a facial expression classification method that is performed by weak classifiers obtained by using new rectangular feature types. In addition, we make the artificial facial expression using the developed robotic system based on biological observation. Finally, experimental results of facial expression recognition and generation are shown for the validity of our robotic system.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.213-220
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• 2023

Face recognition is a very important process in video monitoring and is a type of biometric technology. It is mainly used for identification and security purposes, such as ID cards, licenses, and passports. The recognition process has many variables and is complex, so development has been slow. In this paper, we proposed a face recognition method using CNN, which has been re-examined due to the recent development of computers and algorithms, and compared with the feature comparison method, which is an existing face recognition algorithm, to verify performance. The proposed face search method is divided into a face region extraction step and a learning step. For learning, face images were standardized to 50×50 pixels, and learning was conducted while minimizing unnecessary nodes. In this paper, convolution and polling-based techniques, which are one of the deep learning technologies, were used for learning, and 1,000 face images were randomly selected from among 7,000 images of Caltech, and as a result of inspection, the final recognition rate was 98%.