Library Attendance System using YOLOv5 Faces Recognition

Mardiana, Mardiana; Muhammad, Meizano Ardhi; Mulyani, Yessi

doi:10.1109/iccteie54047.2021.9650628

Cited by 16 publications

(13 citation statements)

References 5 publications

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“…The marker then generates a file name as an output. Lastly, the file scripts are integrated with the information for image preparation [43].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Based Multi Pose Human Face Matching System

Sohail,

Shoukat,

Khan

et al. 2024

IEEE Access

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Current techniques for multi-pose human face matching yield suboptimal outcomes because of the intricate nature of pose equalization and face rotation. Deep learning models, such as YOLO-V5, etc., that have been proposed to tackle these complexities, suffer from slow frame matching speeds and therefore exhibit low face recognition accuracy. Concerning this, certain literature investigated multi-pose human face detection systems; however, those studies are of elementary level and do not adequately analyze the utility of those systems. To fill this research gap, we propose a real-time face matching algorithm based on YOLO-V5. Our algorithm utilizes multi-pose human patterns and considers various face orientations, including organizational faces and left, right, top, and bottom alignments, to recognize multiple aspects of people. Using face poses, the algorithm identifies face positions in a dataset of images obtained from mixed pattern live streams, and compares faces with a specific piece of the face that has a relatively similar spectrum for matching with a given dataset. Once a match is found, the algorithm displays the face on Google Colab, collected during the learning phase with the Robo-flow key, and tracks it using the YOLO-V5 face monitor. Alignment variations are broken up into different positions, where each type of face is uniquely learned to have its own study demonstrated. This method offers several benefits for identifying and monitoring humans using their labeling tag as a pattern name, including high face-matching accuracy and minimum speed of owing face-to-pose variations. Furthermore, the algorithm addresses the face rotation issue by introducing a mixture of error functions for execution time, accuracy loss, frame-wise failure, and identity loss, attempting to guide the authenticity of the produced image frame. Experimental results confirm effectiveness of the algorithm in terms of improved accuracy and reduced delay in the face-matching paradigm.

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“…The marker then generates a file name as an output. Lastly, the file scripts are integrated with the information for image preparation [43].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Based Multi Pose Human Face Matching System

Sohail,

Shoukat,

Khan

et al. 2024

IEEE Access

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“…The system using YOLO took a lot of time to train but it was very accurate and could detect multiple faces at once. The only drawback was the requirement of a high-level GPU or else the model will take a lot of time to train [8]. If one algorithm fails to identify the object, the overall accuracy of the model gets decreased.…”

Section: Discussionmentioning

confidence: 99%

“…An attendance system based on a three sub-system model API service, face recognition using YOLO v5 and visitor identification system was propose [8]. The system took a lot of time to train but it was very accurate and could detect multiple faces at once.…”

Section: Dhirendra Mishramentioning

confidence: 99%

A Systematic Review of Attendance Systems Based on Face Recognition

Modak

Shah

Surana

et al. 2022

PIJR

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In this digital era, face recognition system plays a vital role in almost every sector. Face recognition is one of the most implemented biometrics across various different fields. Classroom attendance check is a contributing factor to student participation and the final success in the courses. Every institute follows their own way for taking attendance.Some are taking attendance manually using papers or a register file or different biometric techniques. Taking attendance by calling out names or passing around an attendance sheet is time-consuming, and the latter is open to easy fraud.In this paper, the comparative analysis of various existing approaches on attendance management system based on facial recognition that are used to monitor attendance in various institutions using Fingerprint,GPS,RFID etc.is discussed with their limitations.

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“…Object tracking dapat diaplikasikan untuk pengawasan lalu lintas, pengenalan tingkah laku dan sebagainya. Object tracking melakukan pelacakan lintasan gerak suatu objek dalam video secara tepat [7], [8]. Multiple Object Tracking (MOT) merupakan sub bagian dari metode object tracking, yang memiliki tujuan untuk melakukan pelacakan banyak objek dalam satu video dan merepresentasikannya menjadi sekumpulan lintasan berakurasi tinggi [6].…”

Section: Pendahuluanunclassified

Sistem Perhitungan Kendaraan Menggunakan Algortima YOLOv5 dan DeepSORT

Cahyani,

Mardiana,

Wintoro

et al. 2024

JuTISI

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Pencemaran udara menjadi isu serius di kota-kota besar, seperti di kota Bandar Lampung. Hal ini diakibatkan oleh tingginya aktivitas transportasi menggunakan kendaraan bermotor. Data tahun 2021 menunjukkan peningkatan 4,30% jumlah kendaraan bermotor di Indonesia, berdampak pada emisi karbon. Sebagai respons terhadap permasalahan tersebut, Greenmetric Universitas Lampung memiliki program kerja transportasi hijau yang berfokus dalam mengurangi emisi kendaraan bermotor. Untuk mendukung tujuan tersebut dilakukan pemantauan lalu lintas secara otomatis dengan menerapkan bidang Computer Vision, yaitu object tracking. Pembuatan sistem object tracking dalam melakukan pemantauan lalu lintas menggunakan kombinasi algoritma YOLOv5 dan DeepSORT. Metode yang digunakan dalam penelitian ini adalah metode Scrum yang dilakukan sebanyak tiga kali sprint, dan dibagi menjadi tiga tahap yaitu pre-game, game, dan post-game. Hasil dari penelitian ini adalah sebuah sistem object tracking yang berhasil membedakan dan menghitung tiga jenis kendaraan (motor, mobil, dan bis) secara otomatis, dan telah dilakukan pengujian secara realtime dengan nilai rata-rata precision sebesar 99%, recall sebesar 97%, F1 score sebesar 97.2%, accuracy sebesar 96.8%, dan rata-rata ketepatan perhitungan sistem sebesar 97.65%.

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Library Attendance System using YOLOv5 Faces Recognition

Cited by 16 publications

References 5 publications

Deep Learning Based Multi Pose Human Face Matching System

Deep Learning Based Multi Pose Human Face Matching System

A Systematic Review of Attendance Systems Based on Face Recognition

Sistem Perhitungan Kendaraan Menggunakan Algortima YOLOv5 dan DeepSORT

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