Implementation of face recognition algorithm for biometrics based time attendance system

Siswanto, Adrian Rhesa Septian; Nugroho, Anto Satriyo; Galinium, Maulahikmah

doi:10.1109/ictss.2014.7013165

Cited by 51 publications

(23 citation statements)

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“…With regard to the attendance system based on face recognition, the minimum number of templates is five [31] while the maximum value is 1000 [59]. Furthermore, the numbers of templates for other face recognition systems are below sixty templates [57,60,61] with most of them having values in between six and twenty-one [29,33,46,53,[62][63][64][65][66][67][68][69]. Next is the authentication stage that involved matching captured biometric data with those templates in the database.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

“…However, most of the facebased systems are contactless, and hence, students do not know when will the attendance be taken by the camera [30,31,49,50,57,63,64,68,70,71]. Nevertheless, there are some cases where the students are mindful that their facial images are being captured, because they are required to face the front of the camera [46,65,72]. In order to authenticate these new data, it is followed by either identification or 3 Journal of Sensors verification process.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

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Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements

Hoo

Ibrahim

2019

Journal of Sensors

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The application of biometric recognition in personal authentication enables the growth of this technology to be employed in various domains. The implementation of biometric recognition systems can be based on physical or behavioral characteristics, such as the iris, voice, fingerprint, and face. Currently, the attendance tracking system based on biometric recognition for education sectors is still underutilized, thus providing a good opportunity to carry out interesting research in this area. As evidenced in a typical classroom, educators tend to take the attendance of their students by using conventional methods such as by calling out names or signing off an attendance sheet. Yet, these types of methods are proved to be time consuming and tedious, and sometimes, fraud occurs. As a result, significant progress had been made to mark attendance automatically by making use of biometric recognition. This progress enables a new and more advanced biometric-based attendance system being developed over the past ten years. The setting-up of biometric-based attendance systems requires both software and hardware components. Since the software and hardware sections are too broad to be discussed in one paper, this literature survey only provides an overview of the types of hardware used. Emphasis is then placed on the microcontroller platform, biometric sensor, communication channel, database storage, and other components in order to assist future researchers in designing the hardware part of biometric-based attendance systems.

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Section: Journal Of Sensorsmentioning

confidence: 99%

Section: Journal Of Sensorsmentioning

confidence: 99%

Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements

Hoo

Ibrahim

2019

Journal of Sensors

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“…Face recognition [16][17][18] is an important research topic in computer vision and has received substantial attention from both research communities and the industry. Nowadays, face recognition has been able to recognize faces with high accuracy in real-time monitoring [19][20][21][22], e.g., face recognition time attendance machines in some companies or supermarkets, intelligent alarm systems of public security organs to track the suspects, intelligent prisoner alarm systems for prison management, security check in train stations or the airport, and so on. Because of its convenient acquisition, fast recognition and user friendliness, face recognition has become a major and widely applied technology of identity recognition [23][24][25].…”

Section: Related Workmentioning

confidence: 99%

Distributed Face Recognition Based on Load Balancing and Dynamic Prediction

Zou

Min

2019

Applied Sciences

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Featured Application: A distributed face recognition system can quickly handle a large number of face recognition tasks, but the problem of load imbalance may lead to time delay and sharp increase of CPU utilization. To deal with the problem, this paper proposes a dynamic load balancing method based on prediction, which can effectively alleviate the time delay and sharp increase of CPU utilization. Distributed face recognition can be widely used in many important research fields and industries such as public security, customs, finance, security. Also, the method proposed in this paper can help provide new ideas for solving task assignment and load balancing problems in other distributed systems such as speech recognition, fingerprint recognition, and vehicle identification.Abstract: With the dramatic expansion of large-scale videos, traditional centralized face recognition methods cannot meet the demands of time efficiency and expansibility, and thus distributed face recognition models were proposed. However, the number of tasks at the agent side is always dynamic, and unbalanced allocation will lead to time delay and a sharp increase of CPU utilization. To this end, a new distributed face recognition framework based on load balancing and dynamic prediction is proposed in this paper. The framework consists of a server and multiple agents. When performing face recognition, the server is used to recognize faces, and other operations are performed by the agents. Since the changes of the total number of videos and the number of pedestrians affect the task amount, we perform load balancing with an improved genetic algorithm. To ensure the accuracy of task allocation, we use extreme learning machine to predict the change of tasks. The server then performs task allocation based on the predicted results sent by the agents. The experimental results show that the proposed method can effectively solve the problem of unbalanced task allocation at the agent side, and meanwhile alleviate time delay and the sharp increase of CPU utilization.

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“…Contohnya, sebuah algoritme yang menganalisis posisi, bentuk, atau ukuran dari mata, hidung, bibir, dan dagu. Hasil dari pengukurannya disimpan ke dalam dataset dan menghasilkan facial metrics [10]. Fitur ini yang akan digunakan untuk template matching pada teknik konvensional.…”

Section: Tinjauan Pustakaunclassified

Kinerja Algoritme Pengenalan Wajah untuk Sistem Penguncian Pintu Otomatis Menggunakan Raspberry-Pi

Budiarto

2017

khif

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Abstrak-Pengenalan wajah mampu memberikan pengalaman interaksi yang paling alami, sebagaimana manusia mampu mengenali manusia lain melalui wajah. Implementasi pengenalan wajah memerlukan biaya yang rendah karena tidak memerlukan alat pengenal khusus selain kamera yang pada saat ini sudah tertanam di berbagai perangkat seperti laptop, smartphone, atau tablet. Pengenalan wajah bukan merupakan tugas mudah bagi komputer. Masalah bertambah ketika komputer diharuskan untuk melakukan klasifikasi wajah dengan berbagai situasi dan kondisi seperti pencahayaan yang gelap, dan tangkapan gambar latar belakang yang ada. Artikel ini mendeskripsikan hasil penelitian sistem pengenalan wajah menggunakan Raspberry Pi yang diterapkan untuk sebuah prototipe pengunci pintu. Metode yang digunakan adalah mengambil sampel dataset kemudian mengevaluasi dan membandingkan algoritme pembelajaran untuk dianalisis tingkat keakuratan dan kecepatan dalam mengenali wajah. Pengujian dilakukan untuk menganalisis metode training dataset yang paling baik untuk diimplementasikan berdasarkan kriteria sensitivitas, spesifisitas, dan false rate. Terdapat 4 buah algoritme yang diuji yakniHasil penelitian menunjukkan algoritme hybrid Eigen-Fisherfaces (PCA-LDA) dan k-nearest neighbor adalah yang metode yang paling akurat untuk pengenalan wajah. Akurasi mendekati 100% dapat diperoleh dengan perhitungan machine learning dengan 4 fold.Kata Kunci: Pengenalan wajah, machine learning, raspberry, eigen fisherfaces, k-nearest neighbor PendahuluanSelama beberapa tahun terakhir, ada banyak sekali pilihan pada teknologi konvensional dan teknologi biometri untuk memenuhi kebutuhan keamanan untuk rumah tangga atau kantor. Beberapa sistem keamanan konvensional, contohnya menggunakan kunci, passcode, kartu ID, dan/atau kartu RFID, bisa jadi tidak dapat diandalkan apabila benda untuk akses tersebut dicuri atau hilang. Sistem keamanan seperti itu memiliki kekurangan ketika akses tersebut dicuri oleh orang yang tidak memiliki wewenang untuk mendapatkan akses [1]. Oleh karena itu, sistem keamanan biometri dianggap sebagai salah satu metode autentikasi yang paling aman, sehingga dapat memberikan tingkat keamanan yang lebih baik dibandingkan dengan sistem keamanan konvensional [2].Fingerprint recognition atau pengenalan sidik jari menjadi metode paling populer dalam teknologi biometri. Metode populer lainnya adalah pengenalan suara, pengenalan tanda tangan, pemindaian iris, dan pemindaian retina [3]. Metode-metode tersebut memberikan tingkat nilai keamanan yang sama karena menggunakan identitas unik pada manusia. Akan tetapi, dalam implementasinya metode-metode tersebut tidak dapat mengalahkan metode pengenalan wajah dalam aspek kenyamanan. Face recognition atau pengenalan wajah mampu memberikan pengalaman interaksi yang paling alami, hal yang serupa ketika manusia mampu mengenali manusia lain melalui wajah. Hal positif lain dalam implementasi pengenalan wajah juga akan mengurangi biaya karena tidak memerlukan alat pengenal khusus selain kamera yang biasanya menjadi perangkat...

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Implementation of face recognition algorithm for biometrics based time attendance system

Cited by 51 publications

References 1 publication

Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements

Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements

Distributed Face Recognition Based on Load Balancing and Dynamic Prediction

Kinerja Algoritme Pengenalan Wajah untuk Sistem Penguncian Pintu Otomatis Menggunakan Raspberry-Pi

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