• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1139-1145
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• 2014

User recognition technology, which identifies or verifies a certain individual is absolutely essential under robotic environments for intelligent services. The conventional face recognition algorithm using single distance face image as training images has a problem that face recognition rate decreases as distance increases. The face recognition algorithm using face images by actual distance as training images shows good performance but this has a problem that it requires user cooperation. This paper proposes the LDA-based long distance face recognition method which uses multiple distance face images from a zoom camera for training face images. The proposed face recognition technique generated better performance by average 7.8% than the technique using the existing single distance face image as training. Compared with the technique that used face images by distance as training, the performance fell average 8.0%. However, the proposed method has a strength that it spends less time and requires less cooperation to users when taking face images.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.11 no.1
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• pp.23-28
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• 2012

In this paper, we proposed and developed a fast and efficient real time face detection and recognition which can be run on embedded system instead of high performance desktop. In the face detection process, we detect a face by finding eye part which is one of the most salient facial features after applying various image processing methods, then in the face recognition, we finally recognize the face by comparing the current face with the prepared face database using a template matching algorithm. Also we optimized the algorithm in our system to be successfully used in the embedded system, and performed the face detection and recognition experiments on the embedded board to verify the performance. The developed method can be applied to automatic door, mobile computing environment and various robot.

• The Journal of the Korea Contents Association
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• v.9 no.2
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• pp.115-124
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• 2009

The popular eigenfaces-based face recognition among proposed face recognition methods utilizes the eigenfaces obtained from applying PCA to a training face image set. Thus, it may not achieve a reliable performance under illumination environments different from that of training face images. In this paper, we propose an illumination-separate eigenfaces-based face recognition method, which excludes the effects of illumination as much as possible. The proposed method utilizes the illumination-separate eigenfaces which is obtained by orthogonal decomposition of the eigenface space of face model image set with respect to the constructed face illumination subspace. Through experiments, it is shown that the proposed face recognition method based on the illumination-separate eigenfaces performs more robustly under various illumination environments than the conventional eigenfaces-based face recognition method.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.9-15
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• 2011

In this paper, we design and implement of gate management system by face recognition using smart phone. We investigate various algorithms for face recognition on smart phones. First step in any face recognition system is face detection. We investigated algorithms like color segmentation, template matching etc. for face detection, and Eigen & Fisher face for face recognition. The algorithms have been first profiled in MATLAB and then implemented on the Android phone. While implementing the algorithms, we made a tradeoff between accuracy and computational complexity of the algorithm mainly because we are implementing the face recognition system on a smart phone with limited hardware capabilities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.208-223
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• 2015

To solve the problem of face recognition with complex changes and further improve the efficiency, a new near-infrared face recognition algorithm which fuses E-GV-LBP and FKNN algorithm is proposed. Firstly, it transforms near infrared face image by Gabor wavelet. Then, it extracts LBP coding feature that contains space, scale and direction information. Finally, this paper introduces an improved FKNN algorithm which is based on spatial domain. The proposed approach has brought face recognition more quickly and accurately. The experiment results show that the new algorithm has improved the recognition accuracy and computing time under the near-infrared light and other complex changes. In addition, this method can be used for face recognition under visible light as well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5605-5623
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• 2016

We propose a face recognition method that utilizes the LCP face descriptor. The proposed method applies a LoG mask to extract a face contour response, and employs the LCP algorithm to produce a binary pattern representation that ensures high recognition performance even under the changes in illumination, noise, and aging. The proposed LCP algorithm produces excellent noise reduction and efficiency in removing unnecessary information from the face by extracting a face contour response using the LoG mask, whose behavior is similar to the human eye. Majority of reported algorithms search for face contour response information. On the other hand, our proposed LCP algorithm produces results expressing major facial information by applying the threshold to the search area with only 8 bits. However, the LCP algorithm produces results that express major facial information with only 8-bits by applying a threshold value to the search area. Therefore, compared to previous approaches, the LCP algorithm maintains a consistent accuracy under varying circumstances, and produces a high face recognition rate with a relatively small feature vector. The test results indicate that the LCP algorithm produces a higher facial recognition rate than the rate of human visual's recognition capability, and outperforms the existing methods.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.39-47
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• 2016

This study is a smart tourism research based on face recognition applied system that manages individual information of foreign tourists to smartphone. It is a way to authenticate by using face recognition, which is biometric information, as a technology applied to identification inquiry, immigration control, etc. and it is designed so that tourism companies can provide customized service to customers by applying algorism to smartphone. The smart tourism system based on face recognition is a system that prepares the reception service by sending the information to smartphone of tourist service company guide in real time after taking faces of foreign tourists who enter Korea for the first time with glasses attached to the camera. The smart tourism based on face recognition is personal information recognition technology, speech recognition technology, sensing technology, artificial intelligence personal information recognition technology, etc. Especially, artificial intelligence personal information recognition technology is a system that enables the tourism service company to implement the self-promotion function to commemorate the visit of foreign tourists and that enables tourists to participate in events and experience them directly. Since the application of smart tourism based on face recognition can utilize unique facial data and image features, it can be beneficially utilized for service companies that require accurate user authentication and service companies that prioritize security. However, in terms of sharing information by government organizations and private companies, preemptive measures such as the introduction of security systems should be taken.

• 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.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2016-2019
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• 2003

In this paper, we take some features for face recognition out of face image, using a simple type of templates. We use the extracted features to do Adaboost learning for face recognition. Using a carefully-chosen feature among these features, we can make a weak face classifier for face recognition. And doing Adaboost learning on and on with those chosen several weak classifiers, we can get a strong face classifier. By using Adaboost Loaming, we can choose particular features which is not easily subject to changes in illumination and facial expression about several images of one person, and construct face recognition system. Therefore, the face classifier bulit like the above way has robustness in both facial expression and illumination variation, and it finally gives capability of recognizing face fast due to the simple feature.

• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.430-437
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• 2020

The proposed model implements a model that improves the face prediction rate and recognition rate through learning with an artificial neural network using face detection, landmark and face recognition algorithms. After landmarking in the face images of a specific person, the proposed model use the previously learned Caffe model to extract face detection and embedding vector 128D. The learning is learned by building machine learning algorithms such as support vector machine (SVM) and deep neural network (DNN). Face recognition is tested with a face image different from the learned figure using the learned model. As a result of the experiment, the result of learning with DNN rather than SVM showed better prediction rate and recognition rate. However, when the hidden layer of DNN is increased, the prediction rate increases but the recognition rate decreases. This is judged as overfitting caused by a small number of objects to be recognized. As a result of learning by adding a clear face image to the proposed model, it is confirmed that the result of high prediction rate and recognition rate can be obtained. This research will be able to obtain better recognition and prediction rates through effective deep learning establishment by utilizing more face image data.