A real time mobile-based face recognition with fisherface methods

Arisandi, Dedy; Syahputra, M F; Putri, I L; Purnamawati, Sarah; Rahmat, R F; Sari, Putri Pradnyawidya

doi:10.1088/1742-6596/978/1/012038

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…Hasil yang didapatkan dari penelitian adalah: 1. Pengolahan Citra Citra merupakan suatu reprentasi (gambar), kemiripan, dan imitasi dari suatu objek (Arisandi et al, 2018). Citra sebagai keluaran suatu sistem perekaman data dapat bersifat optic berupa foto, bersifat analog berupa sinyal-sinyal video seperti gambar pada monitor televisi, atau bersifat duigital yang dapat langsung disimpan pada suatu media penyimpanan.…”

Section: Hasil Dan Pembahasanunclassified

Pengolahan Citra untuk Pengenalan Wajah (Face Recognition) Menggunakan OpenCV

Susim

Darujati

2021

JSA

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Wajah merupakan objek yang umum dalam materi penelitian teknologi computer vision dan image processing, penerapan pengolahan citra dan computer vision mempunyai tugas utama yaitu untuk membuat suatu keputusan tentang objek fisik nyata yang di dapat dari perangkat atau sensor. Untuk membedakan ID wajah yang satu dengan yang lainya butuh beberapa point untuk memilih data pengolahan citra,pengenalan wajah, pendeteksi wajah, penyelarasan wajah dan penyimpanan fitur wajah, algoritma pengenalan wajah menggunakan Eigenface dan diimplementasikan dalam OpenCv. Data berdasarkan sutau contoh citra wajah di cocokkan dengan citra wajah yang tersimpan dalam database yang tersedia dengan mengukur tingkat persamaan macam-macam point, image processing, face recognition, pendeteksi wajah, penyelarasan wajah, ekstraksi wajah, penyimpanan fitur wajah dan pencocokan wajah. Tujuan penelitian ini hanya untuk menerapakan pengenalan wajah (face recognition) pada library OpenCv yang di tulis menggunakan Bahasa pemrograman Python. Rata-rata wajah yang diuji sebanyak 5 citra wajah dapat dikenali dan 2 yang tidak tersimpan karena faktor pencahayaan yang lebih terang, posisi wajah dari jarak dekat dan jauh dari faktor-faktor ini menghasilkan nilai akurasi yang berbeda sesuai dengan dengan tingkat keberhasilan dalam mengenali wajah, dengan tingkat pengenalan rata-rata 85% setelah di proses perbandingan perbandingan hasil kedekatan sekitar 81% untuk kemiripan wajah menggunakan metode PCA Eigenface dapat mengenali seseorang yang terdapat pada database dan tidak dapat mengenali orang yang tidak terdapat dalam database.

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Section: Hasil Dan Pembahasanunclassified

Pengolahan Citra untuk Pengenalan Wajah (Face Recognition) Menggunakan OpenCV

Susim

Darujati

2021

JSA

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“…Directional Local Binary Patterns (dLBP) [34] used the central pixel parameter to determine the neighbours in the same orientation, dLBP proposed in 2015. Arisandi et al [35] developed a real-time phone application for face recognition. The application used Fisherface to recognize students; the accuracy of this application is 90%.…”

Section: Related Workmentioning

confidence: 99%

New Approaches for Automatic Face Recognition Based on Deep Learning Models and Local Handcrafted ALTP

Hattab¹,

Behloul²

2018

ICST Transactions on Scalable Information Systems

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Face recognition is one of the most important topics in biometrics, where it achieved great success under controlled scenarios. Still, its accuracy degraded significantly in unconstrained conditions. To meet this challenge, we proposed a handcraft method based on extracting important regions from the face image. We have been using Scale-Invariant Feature Transform (SIFT) besides the Adaptive Local Ternary Patterns (ALTP). We have achieved an accuracy of 99.75% on the ORL database and 94.70% on the FERET database. Then, we proposed a second method based on deep learning to achieve more accurate face recognition. The deep learning models failed to achieve a high accuracy rate because they require a large amount of training data. We used firstly Data Augmentation to solve this failure. However, this solution does not show high performance. Secondly, our proposed ImageNet pre-trained AlexNet-v2 and VGG16 models with LinearSVC increased the accuracy rate to 100% for the both databases.

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“…Face recognition is a well established field of research with many practical applications [17][18][19][20][21][22][23]. Compressed sensing methods are successfully applied to solve problems in face classification and recognition [24][25][26][27].…”

Section: Face Recognitionmentioning

confidence: 99%

Efficient location, imaging and recognition of faces by single-pixel camera

Roga¹,

Jeffers²

2018

J. Opt.

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Face recognition is a problem with many practical applications. Modern image analysis methods such as object tracking, feature extraction, classification or verification that explore advanced techniques of machine learning and compressive sensing have been used for this purpose. Many of these methods, which are usually applied in image post-processing, are adaptable to fast intelligent viewing with a single-pixel camera. We study such a camera in the context of face recognition with limited information. We seek the optimal basis of patterns for the camera as well as to resolve practical issues related to face localisation and alignment. We compare the use of the Hadamard and eigenface bases for imaging and verification of faces. For the latter task we develop a simple algorithm based on compressive sensing.

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A real time mobile-based face recognition with fisherface methods

Cited by 6 publications

References 4 publications

Pengolahan Citra untuk Pengenalan Wajah (Face Recognition) Menggunakan OpenCV

Pengolahan Citra untuk Pengenalan Wajah (Face Recognition) Menggunakan OpenCV

New Approaches for Automatic Face Recognition Based on Deep Learning Models and Local Handcrafted ALTP

Efficient location, imaging and recognition of faces by single-pixel camera

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