Face recognition based on PCA on wavelet subband

Satone, Manisha; Kharate, G. K.

doi:10.1109/sceecs.2012.6184801

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…Using three levels the image was disassembled. The study showed the third level is the best level of disassembly [14]. In the other work [15], a descriptor is obtained by projecting a face as an input on a eigenface space, then the descriptor is fed as an input to each object's pre-trained network.…”

Section: Related Workmentioning

confidence: 99%

Improving face recognition by artificial neural network using principal component analysis

2020

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The face-recognition system is among the most effective pattern recognition and image analysis techniques. This technique has met great attention from academic and industrial fields because of its extensive use in detecting the identity of individuals for monitoring systems, security and many other practical fields. In this paper, an effective method of face recognition was proposed. Ten person's faces images were selected from ORL dataset, for each person (42) image with total of (420) images as dataset. Features are extracted using principle component analysis PCA to reduce the dimensionality of the face images. Four models where created, the first one was trained using feed forward back propagation learning (FFBBL) with 40 features, the second was trained using 50 features with FFBBL, the third and fourth models were trained using the same features but using Elman neural network. For each person (24) image used as training set for the neural networks, while the remaining images used as testing set. The results showed that the proposed method was effective and highly accurate. FFBBL give accuracy of (98.33, 98.80) with (40, 50) features respectively, while Elman gives (98.33, 95.14) for with (40, 50) features respectively.

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Section: Related Workmentioning

confidence: 99%

Improving face recognition by artificial neural network using principal component analysis

2020

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“…Metode eigenface telah diterapkan untuk mengekstrak wajah dasar gambar wajah manusia [30]. Untuk mengekstrak wajah manusia, digunakan teknik yang sering digunakan yaitu Principal Component Analysis (PCA) [31]. Prinsip utama dari metode ini adalah skor dari komponen utama yang diregresikan dengan variable tak bebas terhadap komponen utama yang saling tak berkorelasi [32].…”

Section: ) Algoritma Eigenfaceunclassified

Perbandingan Waktu Dan Tingkat Akurasi Pada Pengenalan Wajah Dengan Dan Tanpa Menggunakan Dekomposisi Citra

Harjoseputro¹,

Suyoto²,

Dwiandiyanta³

2015

JUTI

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“…Wang, W. et al, [13] combined DWT with SVM, results indicated that the proposed method can achieve recognition precision of 96.78% based on 96 persons in Ren-FEdb database. Satone, M. P, and Kharate, G. K, [14] used Euclidian distance measure, city block distance measure and PCA on DWT, results showed that recognition rate was 94.37% when applied on ORL database. In this paper we study the effect of multi level decomposition of the DWT on a lot of hybrid methods as PCA_SVM, 2D-PCA_FLDA_SVM, FLDA_SVM are studied.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Impact of Wavelet Decomposition on Face Recognition Methods

M.MohieEl-Din¹,

Ghali²,

G³

et al. 2014

IJCA

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Face recognition is the important field of pattern recognition. Discrete Wavelet Transform, (DWT), is known as a very powerful tool in the field of image processing, which extracts the feature vector to determine the performance of face recognition system. However, there are different decomposition levels of DWT. In this paper different decomposition levels of DWT integrated with a lot of good feature extraction methods are examined. Experiments on ORL face database showed that three levels of wavelet decomposition gives promising results and the hybrid method 2D-DWT_PCA_SVM gives high recognition rate and less time rather than other used methods.

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Face recognition based on PCA on wavelet subband

Cited by 6 publications

References 6 publications

Improving face recognition by artificial neural network using principal component analysis

Improving face recognition by artificial neural network using principal component analysis

Perbandingan Waktu Dan Tingkat Akurasi Pada Pengenalan Wajah Dengan Dan Tanpa Menggunakan Dekomposisi Citra

A Study on the Impact of Wavelet Decomposition on Face Recognition Methods

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