Real time recognition of human faces

Sameem, M. Shujah Islam; Qasim, Tehreem; Bakhat, Khush

doi:10.1109/icosst.2016.7838578

Cited by 13 publications

(2 citation statements)

References 5 publications

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“…A novel face discovery and acknowledgement framework was suggested by M Shujah Islam Sameem, et.al [10]. The method had the ability to perceive human faces in single, similar to continuous numerous face pictures in a database.…”

Section: Literature Surveymentioning

confidence: 99%

Security Monitoring System Using FaceNet For Wireless Sensor Network

Preetha¹,

Sheela²

2021

Preprint

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Face Recognition techniques have been rising rapidly over past few decades and gaining attraction in many security systems. Several methodologies are proposed to provide excellent results in face detection. Sensing technology enables several applications such as transportation, healthcare and security. Video surveillance systems trace and identify moving objects. Wireless Sensor networks are used to monitor remote areas and can be applied to monitor a facility by considering each camera as sensor nodes. Cameras are used as nodes in a wireless sensor network with a central server or a gateway node for all the monitoring and analysis of the information retrieved from the nodes. Identification and authentication of users in any organization is quite difficult due to high movement. Face recognition can be used detect faces and identify them continuously in a video feed which can be deployed to continuously monitor an area. Feeding from camera to base station uses Multi-task Cascaded Convolutional Neural Networks (MCTNN) and FaceNet algorithms for face recognition. Further information about the person is sent to all the end-user nodes present in the wireless network. This approach has been implemented and evaluated on a prototype wired camera network called FaceNet to monitor a network and provide security. A method for tracking people in 2D world coordinates and acquiring canonical frontal face images that fits the sensor network paradigm. The approach evaluates and demonstrates the tasking algorithm in action on data acquired from the FaceNet camera network.

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Section: Literature Surveymentioning

confidence: 99%

Security Monitoring System Using FaceNet For Wireless Sensor Network

Preetha¹,

Sheela²

2021

Preprint

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“…Penerapan sistem presensi dengan metode face recognition ini menggunakan konsep computer vision [4] yang merupakan ujung tombak bidang ilmu komputer dengan tujuan untuk mengaktifkan komputer dalam memahami apa yang terjadi pada suatu objek di suatu gambar. Model face recognition secara real time [5] akan lebih memudahkan pengguna dalam melakukan presensi dan juga menghindari adanya manipulasi data presensi. Sehingga mampu mencegah mahasiswa untuk memalsukan kehadirannya dan lebih menghemat waktu dalam pengambilan presensi, oleh karena itu berdasarkan permasalahan di atas maka peneliti membuat suatu perancangan presensi yang dinilai lebih efektif dan berdaya guna yang mampu mengurangi kesalahan dalam proses identifikasi melalui penerapan metode face recognition berbasis OpenCV dengan metode Haar Cascade Classifier dan Local Binary Patterns Histograms (LBPH).…”

Section: Pendahuluanunclassified

Implementasi Penggunaan Opencv Pada Face Recognition Untuk Sistem Presensi Perkuliahan Mahasiswa

Santoso

Kristianto

2020

SISTEMASI

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The lecture data on college lectures into a reference in demonstrating the credibility of each student used by the lecturers as data for the student's value as well as the evaluation of the success of learning activities Teaching in the lecture, but there are some examples of cases, associated with the data of the student's presence that is currently involved in education or lectures is the phenomenon of "absent point". In addition, other problems also arise from the lecturers and administration officers, the difficulties in monitoring student attendance and efforts to validate the presences data because of the number of student data is so much. Therefore in this study submitted a system to reduce the level of fraud in filling the list of the presences and effectiveness of data processing students by using the system implementation of Face Recognition based on Open CV method with The Haar Cascade Classifier and Local Binary Patterns Histograms (LBPH) methods. The results of this Face Recognition study successfully detected when all the users that were reidentified were registered to the system, with the optimal range of Face Recognition to be detected to 150 cm. While Face Recognition is unsuccessful Detected when there is an obstacle covering the face objects and distances exceeding from 150 cm.

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A Comprehensive Review on Face Recognition Methods and Factors Affecting Facial Recognition Accuracy

Anwarul

Dahiya

2019

Lecture Notes in Electrical Engineering

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Real time recognition of human faces

Cited by 13 publications

References 5 publications

Security Monitoring System Using FaceNet For Wireless Sensor Network

Security Monitoring System Using FaceNet For Wireless Sensor Network

Implementasi Penggunaan Opencv Pada Face Recognition Untuk Sistem Presensi Perkuliahan Mahasiswa

A Comprehensive Review on Face Recognition Methods and Factors Affecting Facial Recognition Accuracy

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