• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.101.2-101
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• 2001

This paper proposes a novel face recognition algorithm to recognize human face robustly under various conditions, such as changes of pose, illumination, and expression, etc. at indoor environments. A conventional automatic face recognition system consists of the detection and the recognition part. Generally, the detection part is dominant over the other part in the estimating whole recognition rate. So, in this paper, we suggest the view-specific eigenface method as preprocessor to estimate various poses of the face in the input image. Then, we apply the Enhanced FLD Models (EFM) to the result of it, twice. Because, the EFM recognizes human face, and reduces the error of standardization effectively. To deal with view-varying problem, we build one basis vector set for each view individually. Finally, the dimensionalities of ...

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.123-134
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• 2008

In this paper, we propose a real-tin e face recognition system that is robust under various lighting conditions. Th Modified Census Transform algorithm that is insensitive to illumination variations is employed to extract local structure features. In a practical face recognition system, acquired images through a camera are likely to be blurred and some of them could be side face images, resulting that unacceptable performance could be obtained. To improve stability of a practical face recognition system, we propose a real-time algorithm that rejects unnecessary facial picture and makes use of recognition consistency between successive frames. Experimental results on the Yale database with large illumination variations show that the proposed approach is approximately 20% better than conventional appearance-based approaches. We also found that the proposed real-time method is more stable than existing methods that produces recognition result for each frame.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.4 s.316
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• pp.36-44
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• 2007

In this paper, we implement the speech & face recognition system to support various ubiquitous sensor network application services such as switch control, authentication, etc. using wireless audio and image interface. The proposed system is consist of the H/W with audio and image sensor and S/W such as speech recognition algorithm using psychoacoustic model, face recognition algorithm using PCA (Principal Components Analysis) and LDPC (Low Density Parity Check). The proposed speech and face recognition systems are inserted in a HOST PC to use the sensor energy effectively. And improve the accuracy of speech and face recognition, we implement a FEC (Forward Error Correction) system Also, we optimized the simulation coefficient and test environment to effectively remove the wireless channel noises and correcting wireless channel errors. As a result, when the distance that between audio sensor and the source of voice is less then 1.5m FAR and FRR are 0.126% and 7.5% respectively. The face recognition algorithm step is limited 2 times, GAR and FAR are 98.5% and 0.036%.

• Journal of Intelligence and Information Systems
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• v.10 no.1
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• pp.65-75
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• 2004

It is notorious that PIN(Personal Identification Number) is used widely for user verification and authentication in networked environment. But, when the user Identification and password are exposed by hacking, we can be damaged monetary damage as well as invasion of privacy. In this paper, we adopt face recognition-based authentication which have nothing to worry what the ID and password will be exposed. Also, we suggest the remote authentication and verification system by considering not only 2-Tier system but also 3-Tier system getting be distributed. In this research, we analyze the face feature data using the SVM(Support Vector Machine) and PCA(Principle Component Analysis), and implement artificial intelligence face recognition module in distributed environment which increase the authentication speed and heightens accuracy by utilizing artificial intelligence techniques.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.79-86
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• 2005

In this paper, we propose a new LINF(Linear Independent Non-negative Factorization) algorithm for real-time face recognition systea This system greatly consists of the two parts: 1) face extraction part; 2) face recognition part. In the face extraction Part we applied subtraction image, the detection of eye and mouth region , and normalization method, and then in the face recognition Part we used LINF in extracted face candidate region images. The existing recognition system using only PCA(Principal Component Analysis) showed low recognition rates, and it was hard in the recognition system using only LDA(Linear Discriminants Analysis) to apply LDA directly when the training set is small. To overcome these shortcomings, we reduced dimension as the matrix that had non-negative value to be different from former eigenfaces and then applied LDA to the matrix in the proposed system We have experimented using self-organized DAIJFace database and ORL database offered by AT(')T laboratory in Cambridge, U.K. to evaluate the performance of the proposed system. The experimental results showed that the proposed method outperformed PCA, LDA, ICA(Independent Component Analysis) and PLMA(PCA-based LDA mixture algorithm) method within the framework of recognition accuracy.

• Journal of Convergence Society for SMB
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• v.5 no.3
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• pp.9-13
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• 2015

With the advance of ICT, the necessity of user authentication to verify the identity of an opponent online not face to face is increasing. The authentication, the basis of the security, is used in various fields. Because ID-based authentication has weaknesses in terms of stability and losses, two or more than two authentication tools are used in the place in which the security is important. Recently, biometric authentication rather than ID, OTP, SMS authentication has been an issue in terms of credibility and efficiency. As the fields applied to current biometric recognition technologies are increasing, the application of the biometric recognition is being used in various fields such as mobile payment system, intelligent CCTV, immigration inspection, and access control. As the biometric recognition, finger print, iris, retina, vein, and face recognition have been studied actively. This study is to inspect the current state of domestic and foreign standardization including understanding of the face recognition and the trend of technology.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.174-179
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• 2018

Currently, various security technologies such as fingerprint recognition and face recognition are being developed. However, although many technologies have been developed, the field of incorporating technologies is quite limited. In particular, it is easy to adapt modern security technologies into existing digital systems, but it is difficult to introduce new digital technologies in systems using analog systems. However, if the system can be widely used, it is worth replacing the analog system with the digital system. Therefore, the selected topic is the electronic attendance system. In this paper, a camera is installed to a door to perform a Haar-like feature training for face detecting and real-time face recognition with a Eigenface in principal component analysis(PCA) based face recognition using raspberry pi. The collected data was transmitted to the smartphone using wireless communication, and the application for the viewer who can receive and manage the information on the smartphone was completed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4117-4135
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• 2020

Due to the angle of view, illumination and scene diversity, real-time dynamic face detection and recognition is no small difficulty in those unrestricted environments. In this study, we used the intrinsic correlation between detection and calibration, using a multi-task cascaded convolutional neural network(MTCNN) to improve the efficiency of face recognition, and the output of each core network is mapped in parallel to a compact Euclidean space, where distance represents the similarity of facial features, so that the target face can be identified as quickly as possible, without waiting for all network iteration calculations to complete the recognition results. And after the angle of the target face and the illumination change, the correlation between the recognition results can be well obtained. In the actual application scenario, we use a multi-camera real-time monitoring system to perform face matching and recognition using successive frames acquired from different angles. The effectiveness of the method was verified by several real-time monitoring experiments, and good results were obtained.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.781-784
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• 2017

Face recognition systems are characterized by low invasiveness of acquisition, and increasingly better reliability. Such systems may not be applied effectively, when the images are in low resolution (LR) as in the case that photos are taken from long distances, typically public surveillance. In theory, the high resolution (HR) image reconstructed from an LR face image, applying a super resolution (SR) method, can be used for face recognition. However, existing face SR algorithms may not give satisfactory results in face recognition. This article investigates the very low resolution face recognition problem and introduces a partial differential equation (PDE)-based SR method for a face recognition system of convolutional neural network (CNN).

• Journal of KIISE
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• v.41 no.9
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• pp.666-673
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• 2014

This paper proposes a scalable face recognition method for unconstrained face databases, and shows a simple experimental result. Existing face recognition research usually has focused on improving the recognition rate in a constrained environment where illumination, face alignment, facial expression, and background is controlled. Therefore, it cannot be applied in unconstrained face databases. The proposed system is face feature extraction algorithm for unconstrained face recognition. First of all, we extract the area that represent the important features(landmarks) in the face, like the eyes, nose, and mouth. Each landmark is represented by a high-dimensional LBP(Local Binary Pattern) histogram feature vector. The multi-scale LBP histogram vector corresponding to a single landmark, becomes a low-dimensional face feature vector through the feature reduction process, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis). We use the Rank acquisition method and Precision at k(p@k) performance verification method for verifying the face recognition performance of the low-dimensional face feature by the proposed algorithm. To generate the experimental results of face recognition we used the FERET, LFW and PubFig83 database. The face recognition system using the proposed algorithm showed a better classification performance over the existing methods.