• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.788-789
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• 2023

기존 도어락의 핀 인증 방식은 사용자에 따라 불편함을 느끼기도 한다. 이에 대한 솔루션으로 안면인식 도어락을 제안하며 Jetson nano에 face recognition 딥러닝 모델을 적용한 도어락을 제작한다. 거주자의 이미지를 촬영한 뒤 얼굴의 특징점을 분석하여 저장한다. 사용자가 도어락의 카메라에 인식되었을 때 저장된 안면 정보를 바탕으로 사용자가 거주자의 특징점과 일치하는지 확인한다. 거주자임이 인지되었을 때 도어락은 unlock되어 열리게 된다. 안면 인식 도어락 사용 시 보안상 취약해질 수 있는 기존 도어락의 핀 인증 방식에 대해 보안을 강화할 수 있으며 카메라를 통해 촬영된 사용자의 사진 및 영상을 활용하여 여러 서비스를 적용할 수 있다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.7-12
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• 2022

This paper introduces the implementation of the AWS IoT service and MQTT based smart home system. The smart home system implemented in this study can monitor temperature and humidity, and can manually adjust the air conditioner heating, and can check the visitors with the camera and remotely control the door lock. The implemented smart home system controls door locks, heating and air conditioners using Arduino, and manages the collected data and control information using the AWS IoT service. In this study, the Android app has been developed to allow users to control IoT devices remotely, and the MQTT protocol was used for data communication and control between the app and the AWS IoT server and Arduino. The implemented smart home system has been implemented based on AWS IoT service, which has scalability to add sensors and devices.

• Journal of the Ergonomics Society of Korea
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• v.23 no.3
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• pp.101-109
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• 2004

This paper reports the results on age and gender of Korean elderly people for the level of difficulty in performing household tasks (meal preparation, grocery shopping, house cleaning, laundry), personal tasks (dressing, bathing, grooming), transfer tasks (getting in and out of chairs, getting in and out of bath-tub, using stairs) and management tasks (using telephone, accessing mail, operating door locks). A questionnaire based on the Activities of Daily Living (ADL) scale was constructed and administered to 40 Korean elderly subjects aged from 65 to 84(mean age: 74.5, SD: 5.8) in Busan. Additionally, a logistic regression was performed with age (continuous variable) and gender as predictor variables, and reponses to individual questions as the categorical ordinal response variables. To determine appropriate age separation at which difficulty levels in performing activities of daily living change, a discriminant analysis was performed on the responses. All predictor variables were used in the analysis. Accommodating age related changes in functional abilities, and increasing functional independence of elderly people will entail significant design modifications to products, systems and environments for daily use and living.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.10
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• pp.67-71
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• 2011

Existing face detection systems, which are used for digital door locks, digital cameras, video surveillance systems, and so on, are software-based implementation for relatively low level resolution images. Therefore, in this case, there are difficulties in detecting faces in a real-time fashion due to the increasing amount of operational processing as well as in allowing the requirements of face detections for HD(High Definition) resolutions. A hardware approach is necessary to efficiently find faces for HD images in real-time embedded systems. This paper proposes and implements a hardware architecture for an LBP(Local Binary Pattern) operation which is a time-consuming part as one of preprocessing steps for face detection. The hardware architecture proposed in this research has been implemented and tested with a FPGA(Field Programmable Gate Array) chip, and shown that the approach guarantees efficient face detection for HD images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.613-621
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• 2019

In the case of door locks most widely used in the market, the most used area as a one-dimensional problem is worn out, and a worn area which does not use a special attack method enables password guessing. To solve this problem, various methods such as a keypad for randomly displaying numbers are introduced, but this is also not completely safe. The common feature of all the solutions so far is that the keypad area is fixed. In this paper, we consider that point in reverse and create a new area smaller than the entire area in the entire area of the keypad, making the keypad of the new area move randomly, thereby preventing the password from being deduced. When using this technique, a new type of keypad is proposed for the first time because of the impossibility of a shoulder surfing attack even though the number of keypad is left as it is.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.115-121
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• 2020

With the spread of IoT technology, various IoT applications using facial recognition are emerging. This paper describes the design and implementation of a remote control system using deep learning-based face recognition and hand gesture recognition. In general, an application system using face recognition consists of a part that takes an image in real time from a camera, a part that recognizes a face from the image, and a part that utilizes the recognized result. Raspberry PI, a single board computer that can be mounted anywhere, has been used to shoot images in real time, and face recognition software has been developed using tensorflow's FaceNet model for server computers and hand gesture recognition software using OpenCV. We classified users into three groups: Known users, Danger users, and Unknown users, and designed and implemented an application that opens automatic door locks only for Known users who have passed both face recognition and hand gestures.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.847-857
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• 2020

As IoT equipment is commercialized, Bluetooth or wireless networks will be built into general living devices such as IP cameras, door locks, cars and TVs. Security for IoT equipment is becoming more important because IoT equipment shares a lot of information through the network and collects personal information and operates the system. In addition, web-based attacks and application attacks currently account for a significant portion of cyber threats, and security experts are analyzing the vulnerabilities of cyber attacks through manual analysis to secure them. However, since it is virtually impossible to analyze vulnerabilities with only manual analysis, researchers studying system security are currently working on automated vulnerability detection systems, and Firm-AFL, published recently in USENIX, proposed a system by conducting a study on fuzzing processing speed and efficiency using a coverage-based fuzzer. However, the existing tools were focused on the fuzzing processing speed of the firmware, and as a result, they did not find any vulnerability in various paths. In this paper, we propose IoTFirmFuzz, which finds more paths, resolves constraints, and discovers more crashes by strengthening the mutation process to find vulnerabilities in various paths not found in existing tools.