Feature extraction using Radon and wavelet transforms with application to face recognition

Jadhav, Dattatray V.; Holambe, Raghunath S.

doi:10.1016/j.neucom.2008.05.001

Cited by 74 publications

(39 citation statements)

References 29 publications

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“…Recently, some researchers have developed face recognition algorithms by combining DWT with other methods [16][17][18][19][20]. For example, they combine DWT with fuzzy integral [16,20] or Radon transform [17] to gain better performance than the one produced by the DWT-based face recognition algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Face recognition using decimated redundant discrete wavelet transforms

Tang

Pedrycz

2011

Machine Vision and Applications

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As discrete wavelet transform (DWT) is sensitive to the translation/shift of input signals, its effectiveness could be lessened for face recognition, particularly when the face images are translated. To alleviate drawbacks resulted from this translation effect, we propose a decimated redundant DWT (DRDWT)-based face recognition method, where the decimation-based DWTs are performed on the original signal and its 1-stepshift, respectively. Even though the DRDWT realizes the decimation, it enables us to explore the translation invariant DWT representation for the periodic shifts of the probe image that is the most similar to the gallery images. Therefore, it can solve the problem of translation sensitivity of the original DWT and address the translation effect occurring between the probe image and the gallery image. To further improve the recognition performance, we combine the global wavelet features obtained from the entire face and the local wavelet features obtained from face patches to represent both holistic and detail facial features, apply separate classifiers to global and local features and combine the resulted global and local classifiers to form an ensemble classifier. Experimental results reported for the FERET and FRGCv2.0

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Section: Introductionmentioning

confidence: 99%

“…For example, they combine DWT with fuzzy integral [16,20] or Radon transform [17] to gain better performance than the one produced by the DWT-based face recognition algorithms. The DWT is used mainly owing to its good localized time/frequency characteristics and its ability to deal with abrupt changes, spikes, drifts, and trends.…”

Section: Introductionmentioning

confidence: 99%

Face recognition using decimated redundant discrete wavelet transforms

Tang

Pedrycz

2011

Machine Vision and Applications

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“…Where range of frequency is represented as LL<LH<HL< HH. The feature or characteristics of facial expression is represented by low frequency coefficients or LL sub band so LL frequency sub band is extracted for further feature reduction [15]. …”

Section: Discrete Wavelet Transform Based Feature Extraction Techniquementioning

confidence: 99%

Performance Analysis of Feature Extraction Techniques for Facial Expression Recognition

Neha¹,

Mathur²,

Gupta³

2017

IJCA

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Facial Expression Recognition is a vital topic for research in current scenario which has many applications as machine based HR interviews and human-machine interaction. Facial Expression recognition is applied for identification of person using face of a person. Researchers have proposed many research techniques for facial expression recognition but still accuracy, illumination and occlusion are the research issues which have to improve. So research issues are to improve recognition rate by improving the pre-processing of datasets, improving the feature extraction method and using the best classifier for facial expression recognition. Feature extraction is the key step on which recognition rate depends for facial expression recognition. The purpose of this research work is to analysis of different feature extraction technique in frequency domain as Gabor filter, Discrete Wavelet Transform and Discrete Cosine Transform feature extraction technique. Accuracy is the key research issue in facial expression recognition which is measured in term of Recognition rate.

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“…Haar, French hat, Mexican hat, Daubechies, Coiflet, Symlet, and Ospline).They demonstrated that the performance of the wavelets assessed is similar to that of the Gabor wavelet. Jadhav and Holambe [16] applied radon transform to images and DWT is applied on the generated radon feature space. Radon transform improves the lowfrequency component of the face images and wavelet transform when applied on the Radon feature space provides multiresolution features.…”

Section: Related Workmentioning

confidence: 99%

All Aspects of Digital Video Watermarking Under an Umbrella

Muqeet¹,

Holambe²

2015

IJIGSP

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Abstract-Face recognition using subspace methods are quite popular in research community. This paper proposes an efficient face recognition method based on the application of recently developed triplet half band wavelet filter bank (TWFB) as pre-processing step to further enhance the performance of well known linear and nonlinear subspace methods such as principle component analysis(PCA),kernel principle component analysis (KPCA), linear discriminant analysis (LDA), and kernel discriminant analysis (KDA). The design of 6 th order TWFB is used as the multiresolution analysis tool to perform the 2-D discrete wavelet transform (DWT). Experimental results are performed on two standard databases ORL and Yale. Comparative results are obtained in terms of verification performance parameters such as false acceptance rate (FAR), false rejection rate (FRR) and genuine acceptance rate (GAR). Application of TWFB enhances the performance of PCA, KPCA, LDA, and KDA based methods.

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Feature extraction using Radon and wavelet transforms with application to face recognition

Cited by 74 publications

References 29 publications

Face recognition using decimated redundant discrete wavelet transforms

Face recognition using decimated redundant discrete wavelet transforms

Performance Analysis of Feature Extraction Techniques for Facial Expression Recognition

All Aspects of Digital Video Watermarking Under an Umbrella

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