• International Journal of Advanced Culture Technology
• /
• v.10 no.4
• /
• pp.547-554
• /
• 2022

Due to the expansion of non-face-to-face services, the demand for user identification for mobile devices is increasing. Recently, mobile resident registration cards, mobile driver's licenses, etc. are installed in mobile phones and used for user identification and authentication services. In order to identify a user online, unique identification information of the online user is required. In particular, in order to provide information only to online users, it is necessary to accurately deliver information to a mobile device owned by the user. To make this service possible, it was realized with the advent of mobile electronic notice service. However, the identification of online service users and information on mobile devices owned or subscribed by the relevant users require safe management as personal information, and it is also necessary to increase the convenience of online service users. In this paper, we propose an operating standard for providing a mobile electronic notice service that sends electronic notice using a mobile device owned by the user. The mobile electronic notice service is a service that provides notices expressed in electronic information to the recipient's cell phone, mobile app, e-mail, etc. Therefore, as the use of mobile electronic notification service increases and the provision and use of connecting information to identify users increases, it is necessary to expand the mobile electronic notification service while safely protecting users' personal information.

• International Journal of Computer Science & Network Security
• /
• v.22 no.6
• /
• pp.319-331
• /
• 2022

The latest global COVID-19 pandemic has made the use of facial masks an important aspect of our lives. People are advised to cover their faces in public spaces to discourage illness from spreading. Using these face masks posed a significant concern about the exactness of the face identification method used to search and unlock telephones at the school/office. Many companies have already built the requisite data in-house to incorporate such a scheme, using face recognition as an authentication. Unfortunately, veiled faces hinder the detection and acknowledgment of these facial identity schemes and seek to invalidate the internal data collection. Biometric systems that use the face as authentication cause problems with detection or recognition (face or persons). In this research, a novel model has been developed to detect and recognize faces and persons for authentication using scale invariant features (SIFT) for the whole segmented face with an efficient local binary texture features (DLBP) in region of eyes in the masked face. The Fuzzy C means is utilized to segment the image. These mixed features are trained significantly in a convolution neural network (CNN) model. The main advantage of this model is that can detect and recognizing faces by assigning weights to the selected features aimed to grant or provoke permissions with high accuracy.

• Korean Journal of Optics and Photonics
• /
• v.14 no.4
• /
• pp.383-391
• /
• 2003

A new optical security system for the protection of personal identification information is proposed. Personal identification information consisting of a pure face image and an identification number is used for verification and authentication. Image encryption is performed by a fully phase image encryption technique with two random phase masks located in the input and the Fourier plane of 4-f correlator. The personal information, however, can be leaked out in the decryption process. To cope with this possibility, the encrypted image itself is used in the identification process. An encrypted personal identification number is discriminated and recognized by using the proposed MMACE_p (multiplexed MACE_p) filter, and then authenticity of the personal information is verified by correlation of the face image using the optical wavelet matched filter (OWMF). MMACE_p filter is a synthetic filter with four MACE_p (minimum average correlation energy_phase encrypted) filters multiplexed in one filter plane to recognize 10 different encrypted-numbers at a time. OWMF can improve discrimination capability and SNR (signal to noise ratio). Computer simulations confirmed that the proposed security technique can be applied to the protection of personal identification information.

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

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.3
• /
• pp.99-108
• /
• 2021

There are unrestricted conditions on the driver's face inside the vehicle, such as changes in lighting, partial occlusion and various changes in the driver's condition. In this paper, we propose a face identification system in an unrestricted vehicle environment. The proposed method uses a near-infrared (NIR) camera to minimize the changes in facial images that occur according to the illumination changes inside and outside the vehicle. In order to process a face exposed to extreme light, the normal face image is changed to a simulated overexposed image using mean and variance for training. Thus, facial classifiers are simultaneously generated under both normal and extreme illumination conditions. Our method identifies a face by detecting facial landmarks and aggregating the confidence score of each landmark for the final decision. In particular, the performance improvement is the highest in the class where the driver wears glasses or sunglasses, owing to the robustness to partial occlusions by recognizing each landmark. We can recognize the driver by using the scores of remaining visible landmarks. We also propose a novel robust rejection and a new evaluation method, which considers the relations between registered and unregistered drivers. The experimental results on our dataset, PolyU and ORL datasets demonstrate the effectiveness of the proposed method.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.8 no.2
• /
• pp.31-40
• /
• 2012

Face recognition refers to the process of identifying individuals based on their facial features. It has recently become one of the most popular research areas in the fields of computer vision, machine learning, and pattern recognition because it spans numerous consumer applications, such as access control, surveillance, security, credit-card verification, and criminal identification. However, illumination variation on face generally cause performance degradation of face recognition systems under practical environments. Thus, this paper proposes an novel face recognition system using a fusion approach based on local binary pattern and two-dimensional principal component analysis. To minimize illumination effects, the face image undergoes the local binary pattern operation, and the resultant image are divided into two sub-images. Then, two-dimensional principal component analysis algorithm is separately applied to each sub-images. The individual scores obtained from two sub-images are integrated using a weighted-summation rule, and the fused-score is utilized to classify the unknown user. The performance evaluation of the proposed system was performed using the Yale B database and CMU-PIE database, and the proposed method shows the better recognition results in comparison with existing face recognition techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.11
• /
• pp.5474-5490
• /
• 2017

Although two-dimensional principal component analysis (2DPCA) has been shown to be successful in face recognition system, it is still very sensitive to illumination variations. To reduce the effect of these variations, texture-based techniques are used due to their robustness to these variations. In this paper, we explore several texture-based techniques and determine the most appropriate one to be used with 2DPCA-based techniques for face recognition. We also propose a new distance metric computation in 2DPCA called Row Assembled Matrix Distance (RowAMD). Experiments on Yale Face Database, Extended Yale Face Database B, AR Database and LFW Database reveal that the proposed RowAMD distance computation method outperforms other conventional distance metrics when Local Line Binary Pattern (LLBP) and Multi-scale Block Local Binary Pattern (MB-LBP) are used for face authentication and face identification, respectively. In addition to this, the results also demonstrate the robustness of the proposed RowAMD with several texture-based techniques.

• Journal of the Korea Society for Simulation
• /
• v.20 no.4
• /
• pp.13-20
• /
• 2011

The face recognition has been used in a variety fields, such as identification and security. The procedure of the face recognition is as follows; extracting face features of face images, learning the extracted face features, and selecting some features among all extracted face features. The selected features have discrimination and are used for face recognition. However, there are numerous face features extracted from face images. If a face recognition system uses all extracted features, a high computing time is required for learning face features and the efficiency of computing resources decreases. To solve this problem, many researchers have proposed various Boosting methods, which improve the performance of learning algorithms. Mutual-Boost is the typical Boosting method and efficiently selects face features by using mutual information between two features. In this paper, we propose a GroupMutual-Boost method for improving Mutual-Boost. Our proposed method can shorten the time required for learning and recognizing face features and use computing resources more effectively since the method does not learn individual features but a feature group.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2003.06a
• /
• pp.1816-1819
• /
• 2003

As a result of milling operation, we expect to have burr at the outward edge of workpiece. Also, it causes undesirable problems such as deburring cost, low quality of machined surface, and bottleneck in manufacturing process. Though it is impossible to totally remove burr in machining, it is necessary to plan a machining process that minimizes the occurrence of burr. In this paper, a scheme is proposed which identifies the tool path of the milling operation with minimum burr. In the previous research, a Burr Expert System was developed where the feature identification, the cutting condition identification, and the analysis on exit burr formation are the key steps in the program. The Burr Expert System predicts which portion of workpiece would have the exit burr in advance so that we can calculate the burr length of each milling operation. Here, the critical angle determines whether the burr analyzed is an exit burr or not. So the burr minimization scheme becomes to minimize the burr with critical angle. By iterating all the possible tool paths in certain milling operation, we can identify the tool path with minimum burr.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.2
• /
• pp.346-355
• /
• 2002

This study will offer multimodal recognition instead of an existing monomodal bioinfomatics by using face, lips, to improve the accuracy of recognition. Each bioinfomatics vector can be found by the following ways. For a face, the feature is calculated by principal component analysis with wavelet multiresolution. For a lip, a filter is used to find out an equation to calculate the edges of the lips first. Then by using a thinning image and least square method, an equation factor can be drawn. A voice recognition is found with MFCC by using mel frequency. We＇ve sorted backpropagation neural network and experimented with the inputs used above. Based on the experimental results we discuss the advantage and efficiency.