Pengenalan Wajah Menggunakan Implementasi T-shape Mask  pada Two Dimentional Linear Discriminant Analysis dan Support Vector Machine

Musthafa, Ahmad Reza; Fitrawan, Alif Akbar; Supria, Supria

doi:10.24002/jbi.v7i1.478

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Face recognition involves recognizing a person's face, either manually or with computer software. Computer-assisted facial recognition is utilized as a security system, surveillance tool, and for intelligent human-computer interaction [22]. In general, facial recognition systems can be classified as either feature-based or image-based.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Face Recognition for Patient Identification Using the Transfer Learning Method

2023

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The hospital's status as a health center requires it to ensure patient safety, decrease incidents and treat patients. Identification of the patient is the primary source of patient safety difficulties. In addition to the patient's name and number, further patient-identifying components are needed to reduce this neglect. This work provides a solution in the form of biometric authentication, namely, face recognition. The convolutional neural network (CNN) approach can enable machine facial recognition. CNN is one of the deep learning techniques used to detect and identify picture objects. In this study, facial recognition was carried out using the transfer learning technique, VGGFace2 model pretraining, and SENet 50 model architecture. The dataset was collected via one-shot learning or a single sample per individual sampling. Applying the CNN model to the patient identification system yields two distinct outcomes: patient registration and verification. Registration utilizes a minimum distance of 0.35 and matches data with the complete database, whereas patient verification has a minimum distance of 0.28 and matches only the face in question. At the time of patient registration, the accuracy was between 90% and 100%. However, at the time of patient verification, the accuracy was 100%.

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Section: Related Workmentioning

confidence: 99%

Implementation of Face Recognition for Patient Identification Using the Transfer Learning Method

2023

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“…Each pixel on a facial image is a feature in itself. The larger the image's dimensions, the more features will be processed for facial recognition and the higher the computation time [4]. The greater the amount of training data will also increase the computational time [5].…”

Section: Introductionmentioning

confidence: 99%

Development of a Facial Recognition-based Attendance System using Binary Patterns Histograms Method and Telegram Bot Notification

Fauzan

Budianto

Budiyanto

2022

JOIVE

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In carrying out attendance activities, most schools still use the manual attendance method, which utilizes the attendance book as a medium. However, the presence of the manual still causes some problems that arise when using this method. With the aim of this study, a "Facial Recognition-Based Attendance System" was created. This study used the Local Binary Pattern Histogram (LBPH) method to detect and recognize faces. The system is made to recognize the student's face and name, which the attendance system will then carry out along with the student's identity in the form of Name and Absence Number in real time to find out that the student is present in the class. The names that have been diabase can be saved through the XLS format. The result of this study is a facial recognition-based attendance system using the LBPH (Local Binary Pattern Histogram) method. The system is web-based, so users can easily access it through the internet. This research method uses research and development methods. This research stage consists of research and information collection, planning, and product draft development. The results of this study showed the feasibility level of facial recognition-based attendance systems. The eligibility rate obtained showed a score of 78.98%. From these results, this system is worthy of being used as an attendance system.

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Pengenalan Wajah Menggunakan Implementasi T-shape Mask pada Two Dimentional Linear Discriminant Analysis dan Support Vector Machine

Cited by 2 publications

References 7 publications

Implementation of Face Recognition for Patient Identification Using the Transfer Learning Method

Implementation of Face Recognition for Patient Identification Using the Transfer Learning Method

Development of a Facial Recognition-based Attendance System using Binary Patterns Histograms Method and Telegram Bot Notification

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