Face Recognition using Neuro-Fuzzy Inference System

Mehta, Shweta; Gupta, Shailender; Bhushan, Bharat; Nagpal, Chaitanya

doi:10.14257/ijsip.2014.7.1.31

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Many research works have been done on eyeglasses recognition, removal, and localization [12], [13]. Jiang et al [14] have used an eyeglasses classifier for its detection on facial images, for determining glasses on facial images only six measures were used.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-time eyeglass detection using transfer learning for non-standard facial data

Jain

Goyal

Kalaichelvi

2022

IJECE

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<span>The aim of this paper is to build a real-time eyeglass detection framework based on deep features present in facial or ocular images, which serve as a prime factor in forensics analysis, authentication systems and many more. Generally, eyeglass detection methods were executed using cleaned and fine-tuned facial datasets; it resulted in a well-developed model, but the slightest deviation could affect the performance of the model giving poor results on real-time non-standard facial images. Therefore, a robust model is introduced which is trained on custom non-standard facial data. An Inception V3 architecture based pre-trained convolutional neural network (CNN) is used and fine-tuned using model hyper-parameters to achieve a high accuracy and good precision on non-standard facial images in real-time. This resulted in an accuracy score of about 99.2% and 99.9% for training and testing datasets respectively in less amount of time thereby showing the robustness of the model in all conditions.</span>

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Section: Literature Reviewmentioning

confidence: 99%

Real-time eyeglass detection using transfer learning for non-standard facial data

Jain

Goyal

Kalaichelvi

2022

IJECE

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“…Di dalam pengolahan citra, terdapat beberapa metode yang biasa digunakan di dalam sebuah aplikasi, yaitu image enhancement yang merupakan metode untuk membuat sebuah gambar menjadi lebih baik dan mengurangi noise pada gambar tersebut [2,3,7,8], image segmentation merupakan metode untuk menyederhanakan gambar yang sudah melewati proses enhancement agar lebih mudah untuk dianalisis [4,9], dan features extraction merupakan metode yang digunakan untuk mendeteksi bentuk di dalam sebuah objek, dan metode ini juga berfungsi untuk mendeteksi adanya keanehan di dalam sebuah gambar [2]. Dan dengan perkembangan teknologi saat ini, pengolahan citra dengan metode di atas dapat digabungkan dengan sebuah sistem yang dikenal dengan neural network, dimana gabungan sistem ini akan lebih mudah dan akurat di dalam mendeteksi sebuah objek yang selalu dipengaruhi oleh faktor luar, seperti cahaya, posisi dan umur [10,11,12,13,14].…”

Section: Pendahuluanunclassified

Desain Sistem Pendeteksi untuk Citra Base Sub-assembly dengan Algoritma Backpropagation

Kasdianto¹,

Aisyah²

2017

JRE

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Teknik identifikasi objek menggunakan mesin berbasis vision telah banyak diimplementasikan pada industri manufaktur elektronika. Teknik ini kebanyakan digunakan untuk tujuan deteksi reject (produk yang tidak sesuai standar) atau deteksi defect (cacat). Penelitian ini bertujuan membuat alat deteksi kondisi reject subassembly base yang dibedakan menjadi dua kondisi yaitu missing screw dan wrong position screw, menggunakan metode jaringan syaraf tiruan backpropagation. Citra yang diambil menggunakan kamera akan diubah ke dalam grayscale terlebih dahulu sebelum diimplementasikan dengan metode backpropagation untuk menghasilkan nilai bobot. Hasil pengujian menunjukkan bahwa arsitektur jaringan backpropagation dengan 2 layer memiliki tingkat akurasi paling baik. Dengan learning rate 0.5, target error 0.015%, jumlah node 1 sebesar 100 dan node 2 sebesar 50, tingkat keberhasilan deteksi objek Sub-assembly kondisi benar mencapai 99.02% dengan tidak ada error pada proses deteksi sub-assembly kondisi salah (missing screw dan wrong position screw).

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“…Then the NN is training based on the data to regulate thresholds and weights of the network's to reduce predictions error [20].…”

mentioning

confidence: 99%

Neuro-fuzzy inference system based face recognition using feature extraction

Abdullah

2020

TELKOMNIKA

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Human face recognition (HFR) is the method of recognizing people in images or videos. There are different HFR methods such as feature-based, eigen-faces, hidden markov model and neural network (NN) based methods. Feature extraction or preprocessing used in first three mentioned methods that associated with the category of the image to recognize. While in the NN method, any type of image can be useful without the requirement to particular data about the type of image, and simultaneously provides superior accuracy. In this paper, HFR system based on neural-fuzzy (NF) has been introduced. In the NN system, backpropagation (BP) algorithm is used to update the weights of the neurons through supervised learning. Two sets of the image have been used for training and testing the network to identify the person. If the test image matches to one of the trained sets of the image, then the system will return recognized. And if the test image does not match to one of the trained sets of the image, then the system will return not recognized. The feature extraction methods used in this paper is Geometric moments and Color feature extraction. The recognition rate of 95.556 % has been achieved. The experimental result illustrations that the association of two techniques that provide better accuracy.

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Face Recognition using Neuro-Fuzzy Inference System

Abstract: Abstract

Cited by 7 publications

References 23 publications

Real-time eyeglass detection using transfer learning for non-standard facial data

Real-time eyeglass detection using transfer learning for non-standard facial data

Desain Sistem Pendeteksi untuk Citra Base Sub-assembly dengan Algoritma Backpropagation

Neuro-fuzzy inference system based face recognition using feature extraction

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