Automatic Attendance System Using Face Recognition Technique

Agarwal, Rakshanda; Jain, Rishabh; Regunathan, Rajeshkannan; Kumar, C. S. Pavan

doi:10.1007/978-981-13-1610-4_53

Cited by 13 publications

(4 citation statements)

References 13 publications

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“…Setelah wajah terdeteksi maka bagian wajah akan dipotong seperti pada Gambar 3 untuk masing-masing wajah yang terdeteksi. Program dapat mendeteksi lebih dari 1 wajah pada gambar, program akan menyimpan hasil potongan gambar dalam array untuk masing-masing gambar dan tiap gambar akan dianalisis secara independen [13]. Proses pengenalan wajah dapat dilihat pada Gambar 4.…”

Section: B Deteksi Wajahunclassified

Sistem Absensi Berbasis Pengenalan Wajah

Miyata

Pranjoto

2022

teknika

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Virus COVID-19 menyebar melalui droplet dari mulut dan hidung yang dapat menyebar melalui sentuhan. Sistem absensi yang biasa digunakan berbasis sentuhan seperti sidik jari maupun Radio-frequency identification (RFID). Untuk mengurangi penyebaran virus COVID-19 diperlukan absensi yang tidak berbasis sentuhan, karena itu dipilih sistem absensi menggunakan wajah. Untuk dapat mengenali wajah dibutuhkan metode guna mendeteksi keberadaan wajah, kemudian mendapatkan data orthoghonal dari wajah. Data orthogonal dari 2 foto orang yang sama akan memiliki kesamaan 60-80%, sedangkan orang yang berbeda akan dibawah 60%. Program mempunyai akurasi 95% saat melihat orang yang terdaftar pada database namun akurasi berkurang saat melihat orang yang tidak terdapat pada database menjadi sebesar 80% saat dilakukan uji coba menggunakan 20 orang yang tidak terdaftar. Lebih susah bagi sistem untuk mengenali bahwa orang tersebut tidak terdaftar karena apabila batas bawah kecocokan ditingkatkan akan berpengaruh dengan kemampuan sistem mendeteksi wajah orang yang terdaftar. Untuk meningkatkan akurasi dapat dilakukan dengan cara memiliki database foto resolusi tinggi, pencahayaan mencerminkan ruangan yang digunakan untuk absensi, dan menggunakan gambar terbaru.

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Section: B Deteksi Wajahunclassified

Sistem Absensi Berbasis Pengenalan Wajah

Miyata

Pranjoto

2022

teknika

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“…Key frame-based saliency detection and real-time action with 3D deep learning are also part of HAR [41,42]. The arrangement of HAR is created as structures to enable the constant checking and examination of human practices in various zones, for example, clever human action and conduct investigation intelligent human activity and behavior analysis [33,[43][44][45][46], sports injury detection [47], patient rehabilitation [33], monitor activity shifts amid elderly citizens that might be helpful to detect and diagnose serious illness [48,49], monitoring children's surveillance, hospital/patient monitoring [50,51], recognition and classification of the human usual and unusual activities [26,[52][53][54][55][56], human behavior recognition and human activity detection [53,57,58], criminal tracking system [59,60], automatic attendance system [61,62], etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet With Entropy Coded Ant Colony System Optimization

et al. 2021

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Intelligent visual surveillance systems are attracting much attention from research and industry. The invention of smart surveillance cameras with greater processing power has now been the leading stakeholder, making it conceivable to design intelligent visual surveillance systems. It is possible to assure the safety of people in both homes and public places. This work aims to distinguish the suspicious activities for surveillance environments. For this, a 63 layers deep CNN model is suggested and named "L4-Branched-ActionNet". The suggested CNN structure is centered on the alteration of AlexNet with added four blanched sub-structures. The developed framework is first transformed into a pre-trained framework by conducting its training on an object detection dataset called CIFAR-100 with the SoftMax function. The dataset for suspicious activity recognition is then forwarded to this pretrained model for feature acquisition. The acquired deep features are subjected to feature subset optimization. These extracted features are first coded by applying entropy and then an ant colony system (ACS) is utilized on the entropy-based coded features for optimization. The configured features are then fed into numerous SVM and KNN based classification models. The cubic SVM has the highest efficiency scores, with a performance of 0.9924 in terms of accuracy. The proposed model is also validated on the Weizmann action dataset and attained an accuracy of 0.9796. The successful findings indicate the suggested work's soundness.

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“…Pattern recognition is one of media that has used it for a long time in several organizations, considering that the attendance system uses fingerprints that are difficult to represent [7][8][9]. Another alternative choice is to use Face Recognition to overcome problems in the attendance system, where users will find it difficult to cheat in terms of attendance [7,[10][11][12].…”

Section: Literature Reviewsmentioning

confidence: 99%