Face recognition in an intelligent door lock with ResNet model based on deep learning

Phawinee, Suphawimon; Cai, Jianfei; Guo, Zhe-Yu; Zheng, Hao-Ze; Chen, Guan-Chen

doi:10.3233/jifs-189624

Cited by 11 publications

(5 citation statements)

References 1 publication

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“…This paper proposes a dynamic security authentication protocol based on the above extracted staff biometric information data, which is bound to the staff identity ID. In this security protocol, the authentication key [10] is established in the form of symmetric polynomial by assigning and managing the key and updating it dynamically. And the generated results are stored in different heterogeneous identification information databases, and the mapping of identification information between the two databases is matched to ensure that the key can effectively protect the transmission path between the staff and the device, and the specific parameters of the entire security protocol are described in Table 1.…”

Section: Power System Operation and Maintenance Scenario Security Aut...mentioning

confidence: 99%

Security authentication method for power monitoring system operation and maintenance scenarios based on face recognition and fingerprint recognition

Sun,

Ye,

Wang

et al. 2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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The current conventional security authentication methods for power monitoring system operation and maintenance scenarios mainly verify the user's identity by means of certificate authentication or password authentication, which leads to poor recognition accuracy due to the lack of effective extraction of key user information. For that, the security authentication method of power monitoring system operation and maintenance scene based on face recognition and fingerprint recognition is proposed. The biometric information of staffs in power system operation and maintenance scenes is obtained through face detection and fingerprint morphological feature extraction. The security authentication protocol is also designed, and the data interaction behavior is encrypted in the form of session key, and the security authentication model is constructed on this basis. In the experiment, the designed security authentication method is tested for recognition effect. The final results can prove that when the proposed method is used to identify user personnel information in the power monitoring system, the identification accuracy is high and has a more desirable authentication effect.

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Section: Power System Operation and Maintenance Scenario Security Aut...mentioning

confidence: 99%

Security authentication method for power monitoring system operation and maintenance scenarios based on face recognition and fingerprint recognition

Sun,

Ye,

Wang

et al. 2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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“…The system performs real-time face recognition and quickly detects when a face is in front of a camera. The face recognition system was developed based on machine learning, with a Haar graphics recognition program built into OpenCV in [ 10 ]. Also, the system used deep learning methods such as VGG-16, VGG-19, and ResNet50.…”

Section: Introductionmentioning

confidence: 99%

A New Framework for Smart Doors Using mmWave Radar and Camera-Based Face Detection and Recognition Techniques

Akbari,

Al-Binali,

Al-Mohannadi

et al. 2023

Sensors

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By integrating IoT technology, smart door locks can provide greater convenience, security, and remote access. This paper presents a novel framework for smart doors that combines face detection and recognition techniques based on mmWave radar and camera sensors. The proposed framework aims to improve the accuracy and some security aspects arising from some limitations of the camera, such as overlapping and lighting conditions. By integrating mmWave radar and camera-based face detection and recognition algorithms, the system can accurately detect and identify people approaching the door, providing seamless and secure access. This framework includes four key components: person detection based on mmWave radar, camera preparation and integration, person identification, and door lock control. The experiments show that the framework can be useful for a smart home.

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“…The traditional way of returning books in university libraries is that the returner puts his valid borrowing certificate in the designated sensing area to identify his identity, returns the book, and takes it away [1][2]. The whole process of returning books in the library is cumbersome, and book returners often forget to carry valid borrowing certificates, which lead to the failure of book return and wastes the time and energy of book returners [3].…”

Section: Introductionmentioning

confidence: 99%

Research on Library Face Book Return Model Based on Hybrid PCA and Kernel Function

Shi¹

2023

IJACSA

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With the improvement in the quality of university education in China, the behavior of college students and school teachers to borrow and return books in the library is becoming more and more frequent. In peak periods of returning books, managers cannot even assist in returning books in time. Therefore, this research uses kernel function, multi-dimensional principal component analysis method, and multi-dimensional linear discriminant analysis method to construct a new face recognition algorithm for the automatic return of books in the university library. The test results show that the XT_2D_PL algorithm designed in this study has a face recognition rate of 96.8%. When the number of face samples of each type in the test sample set is 11, and when the number of feature dimensions is 14, the recognition rate of 96.3% reaches the highest level. However, if the sample to be processed is 500 pictures, the calculation speed is 1.072ms/per photo, higher than most comparison algorithms. The proposed face recognition algorithm has high recognition accuracy on the library face data; the calculation speed meets the needs of practical applications, and has certain practical promotion potential.

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Face recognition in an intelligent door lock with ResNet model based on deep learning

Cited by 11 publications

References 1 publication

Security authentication method for power monitoring system operation and maintenance scenarios based on face recognition and fingerprint recognition

Security authentication method for power monitoring system operation and maintenance scenarios based on face recognition and fingerprint recognition

A New Framework for Smart Doors Using mmWave Radar and Camera-Based Face Detection and Recognition Techniques

Research on Library Face Book Return Model Based on Hybrid PCA and Kernel Function

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