• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.269-279
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• 2022

Purpose: The purpose of this study is to develop a deep learning-based personal protective equipment detection model for disaster prevention at construction sites, and to apply it to actual construction sites and to analyze the results. Method: In the method of conducting this study, the dataset on the real environment was constructed and the developed personal protective equipment(PPE) detection model was applied. The PPE detection model mainly consists of worker detection and PPE classification model.The worker detection model uses a deep learning-based algorithm to build a dataset obtained from the actual field to learn and detect workers, and the PPE classification model applies the PPE detection algorithm learned from the worker detection area extracted from the work detection model. For verification of the proposed model, experimental results were derived from data obtained from three construction sites. Results: The application of the PPE recognition model to construction site brings up the problems related to mis-recognition and non-recognition. Conclusions: The analysis outcomes were produced to apply the object recognition technology to a construction site, and the need for follow-up research was suggested through representative cases of worker recognition and non-recognition, and mis-recognition of personal protective equipment.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.831-832
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• 2008

This paper proposes the experimental results and analysis of face recognition on an conventional UMPC(Ultra Mobile Personal Computer). With face images acquired by the embedded camera of UMPC, we detected the facial region by using Adaboost face detector. The detected image was normalized into a $32{\times}32$ pixel sized image for face recognition. We performed face recognition based on PCA (Principal Component Analysis). As experimental results, the TER (Total Error Rate) of face recognition was 19.77%.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2935-2937
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• 1999

The human has recognizable part of body such as a fingerprint, a crimson, a blood vessel. This part has been investigated constantly, its confidence for personal recognition is high. In spite of specialized part of human body, a lip print recognition is developed less than the other physical attribute that is a fingerprint. a voice pattern, a retinal blood-vessel pattern, or a facial recognition. This paper is to implement hardware for lip print recognition system using VHDL.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.137-140
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• 2002

We introduce a personal identification method which can find user's ID without any help of the user. To do this, there has been two approaches, vision-based and pressure-based. Pressure-based approach has some advantages compared than vision-based one in the aspects of illumination, occlusion, and the amount of data. In the previous study about pressure-based personal identification, there are some restrictions about human body posture for extracting normalized footprints. Since this approach cannot be extended unconscious and continuos identification, we suppose more natural method and verified it by experiments.

• Journal of KIISE:Software and Applications
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• v.33 no.1
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• pp.130-137
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• 2006

The depth information in the face represents personal features in detail. In particular, the surface curvatures extracted from the face contain the most important personal facial information. This surface curvature and together with grouped projection vector which reduces the dimensions resulting less computations are collaborated into the proposed 3D face recognition algorithm. The maximum and minimum curvature are calculated from the surface curvature image, which are grouped into projected vectors for recognition. The minimum curvature showed the best recognition rate among the surface parameters.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.3
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• pp.1-7
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• 2012

As personal information is utilized as an important user authentication means, a trustable certification means is being required. Recently, a research on the biometrics system using a part of the human body like a password is being attempted a lot. The face recognition technology using characteristics of the personal face among several biometrics technologies is easy in extracting features. In this paper, we implement a face recognition system for unattended reception interface. Our method is performed by two steps. Firstly the face is extracted using Haar-like feature method. Secondly the method combining PCA and LDA for face recognition was used. To assess the effectiveness of the proposed system, it was tested and experimental results show that the proposed method is applicable for unattended reception interface.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.365-367
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• 2016

Recently, the importance of personal information security has emerged. In particular, the importance of biometrics such as voice recognition, fingerprint recognition, face recognition has been highlighted in various fields such as access control, banking security, personal PC security and so on. But if such conventional security techniques are likely to be recognized by an external factor failure. In contrast, if the vein recognition is impossible to copy and counterfeit. Therefore less likely to fail due to external factors has the advantage. In this paper, propose a preprocessing method to improve on the existing vein pattern recognition with high reliability than existing biometric technologies.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.730-744
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• 2023

The topic of this paper is the recognition of human activities using egocentric vision, particularly captured by body-worn cameras, which could be helpful for video surveillance, automatic search and video indexing. This being the case, it could also be helpful in assistance to elderly and frail persons for revolutionizing and improving their lives. The process throws up the task of human activities recognition remaining problematic, because of the important variations, where it is realized through the use of an external device, similar to a robot, as a personal assistant. The inferred information is used both online to assist the person, and offline to support the personal assistant. With our proposed method being robust against the various factors of variability problem in action executions, the major purpose of this paper is to perform an efficient and simple recognition method from egocentric camera data only using convolutional neural network and deep learning. In terms of accuracy improvement, simulation results outperform the current state of the art by a significant margin of 61% when using egocentric camera data only, more than 44% when using egocentric camera and several stationary cameras data and more than 12% when using both inertial measurement unit (IMU) and egocentric camera data.

• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.528-538
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• 2003

A SCCRS (Speech and Character Combined Recognition System) is developed for working on mobile devices such as PDA (Personal Digital Assistants). In SCCRS, the feature extraction is separately carried out for speech and for hand-written character, but the recognition is performed in a common engine. The recognition engine employs essentially CHMM (Continuous Hidden Markov Model), which consists of variable parameter topology in order to minimize the number of model parameters and to reduce recognition time. For generating contort independent variable parameter model, we propose the SSMS(Successive State and Mixture Splitting), which gives appropriate numbers of mixture and of states through splitting in mixture domain and in time domain. The recognition results show that the proposed SSMS method can reduce the total number of GOPDD (Gaussian Output Probability Density Distribution) up to 40.0% compared to the conventional method with fixed parameter model, at the same recognition performance in speech recognition system.

• Journal of the Korean Home Economics Association
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• v.30 no.4
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• pp.231-247
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• 1992

A Study on Maternal Verbal Control Patterns and Children's Recognition against those. The primary objective of the material presented in this dissertation is the analysis of the maternal verbal control patterns. The major focus is given to the differences between maternal verbal control patterns and children's recognition against those. The purspoe of this study were; 1) to investigate the maternal verbal control patterns recognized by children according to their family and personal surroundings; 2) to investigate the verbal control patterns in mother's response according to the differences of family and personal surroundings; 3) to find the differences between children's recognition and maternal verbal control patterns; 4) How the maternal verbal control patterns recognized by children are? The materials of this study were 81(37; boys, 44; girl)) from kindergarten in Seou. The sample of age used in this study was 5 to 7 years old. The test about maternal verbal environment used the question sheet for reaction between mother and children by Gumperz. The kids got interviews with same questions as mothers with question sheet. The statistical tools used is the analysis of the data were frequency, crosstab, and t-test. The result of the study were summarized as follows; 1) The maternal verbal control patterns recognized by children had differences according to whether living with grand-parent or not. Living with grand-parent are more person-oriented than without grand-parent group. 2) The maternal verbal control patterns of mother response show the differences within mother's academic background, present situation of child, and parent's expection to child. The imperative verbal control pattern was more frequently used of low academic background who just fraduate the middle or high school. The personal verbal control pattern was more frequently used of high educational background's like university graduate or more educated group. 3) The differences between maternal verbal control patterns with children's recognition and with mother response is that mother responseis more person-oriented than children's. 4) The imperative verbal control pattern used command, verbal purnishment, and physical purnishment. the status-oriented verbal control pattern used univesal positional appeals and limited positional appeals. The person-oriented verbal control pattern used child-oriented cognitive affective appeals.