An improved 2DPCA algorithm for face recognition

Gan, Junying; He, Shaoming

doi:10.1109/icmlc.2009.5212240

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Then they have compared with traditional PCA method, which their result has shown moreacceptable to process samples with different class and same class.Therefore, this experimental showswhich, a higher correct recognition rate can be acquired, then a better efficiency can be achieved [7]. …”

Section: Related Workmentioning

confidence: 99%

A Comparative Studyon Kernel PCA and PCA Methods for Face Recognition

2016

IJSR

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Section: Related Workmentioning

confidence: 99%

A Comparative Studyon Kernel PCA and PCA Methods for Face Recognition

2016

IJSR

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“…Pengenalan wajah adalah suatu kegiatan yang aktif di bidang biometric [24]. Bagian terpenting dalam pengenalan wajah adalah pendeteksian bagian -bagian dari wajah [25].…”

Section: B Landasan Teori 1) Pengenalan Wajah (Face Recognition)unclassified

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

Harjoseputro¹,

Suyoto²,

Dwiandiyanta³

2015

JUTI

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“…DWT, see for example [19][20][21], is normally used in signal and image processing in order to obtain the useful information from the signal or image that can be used for feature extraction stage. The Principal Component Analysis (PCA) is widely used in image processing, particularly in the area of face recognition and compression [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

Elgargni

Al‐Habaibeh

Lotfi

2014

Int J Adv Manuf Technol

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The implementation of computerised condition monitoring systems for the detection cutting tools' correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a noncontact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool's condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms.

show abstract

An improved 2DPCA algorithm for face recognition

Cited by 5 publications

References 7 publications

A Comparative Studyon Kernel PCA and PCA Methods for Face Recognition

A Comparative Studyon Kernel PCA and PCA Methods for Face Recognition

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

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

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