Face Recognition by Multi-resolution Curvelet Transform on Bit Quantized Facial Images

Majumdar, A. K.; Bhattacharya, Avik

doi:10.1109/iccima.2007.12

Cited by 6 publications

(9 citation statements)

References 10 publications

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“…The motivation behind this extension follows from some recent work [10,11], where it was shown that better face recognition results can be obtained in wavelet domain compared to the spatial domain. Also the use of curvelet [12] and contourlet [13] showed promising results in face recognition. We believe that by using transform coefficients instead of the pixel values of the images, better recognition accuracy can be achieved.…”

Section: Fig 1 Recognition Schemementioning

confidence: 99%

Pseudo-Fisherface method for single image per person face recognition

Majumdar

Ward

2008

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

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The problem of recognizing a face from a single sample available in a stored dataset is addressed. A new method of tackling this problem by using the Fisherface method on a generic dataset is explored. The recognition scheme is also extended to multiscale transform domains like wavelet, curvelet and contourlet. The proposed method in the transform domain shows better recognition errors than the SPCA algorithm and Eigenface Selection method, both of which are specially tailored for recognizing faces from single samples.

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Section: Fig 1 Recognition Schemementioning

confidence: 99%

Pseudo-Fisherface method for single image per person face recognition

Majumdar

Ward

2008

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…Though there are several basic results on image denoising, face recognition and palm print recognition using the curvelet features [9] [19][17] [28]. To best of our knowledge, there is still no research on facial expression recognition with curvelet transform until now.…”

Section: Introductionmentioning

confidence: 99%

A Novel Facial Expression Recognition Based on the Curvelet Features

Zhou

Wang

et al. 2010

2010 Fourth Pacific-Rim Symposium on Image and Video Technology

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Curvelet transform has been recently proved to be a powerful tool for multi-resolution analysis on images. In this paper we propose a new approach for facial expression recognition based on features extracted via curvelet transform. First curvelet transform is presented and its advantages in image analysis are described. Then the coefficients of curvelet in selected scales and angles are used as features for image analysis. Consequently the Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are used to reduce and optimize the curvelet features. Finally we use the nearest neighbor classifier to recognize the facial expressions based on these features. The experimental results on JAFFE and Cohn Kanade two benchmark databases show that the proposed approach outperforms the PCA and LDA techniques on the original image pixel values as well as its counterparts with the wavelet features.

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“…Next Least Square Support Vector Machine (LS-SVM) was trained using Curvelet features. The results in [11,12,13,14,15] have showed Curvelet based schemes were better than wavelet based recognition schemes [9].…”

Section: Introductionmentioning

confidence: 99%

“…K-Nearest Neighbor classifier was employed to perform the classification task. In [11] as preprocessing step researchers converted face images from 8 bit into 4 bit and 2 bit representations. Curvelet transform was performed to extract feature vectors from these representations, and then the approximated components were used to train different SVMs.…”

Section: Introductionmentioning

confidence: 99%

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DIC Structural HMM based IWAK-means to Enclosed Face Data

Alhanjouri¹,

Hejazi²

2011

IJCA

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This paper identifies two novel techniques for face features extraction based on two different multi-resolution analysis tools; the first called curvelet transform while the second is waveatom transform. The resultant features are trained and tested via three improved hidden Markov Model (HMM) classifiers, such as: Structural HMM (SHMM), Deviance Information CriterionInverse Weighted Average K-mean-SHMM (DIC-IWAK-SHMM), and Enclosed Model Selection Criterion (EMC) coupled with DIC-IWAK-SHMM as the proposed methods for face recognition.A comparative studies for DIC-IWAK-SHMM approach to recognize the face ware achieved by using two type of features; one method using Waveatom features and the other method uses 2-level Curvelet features, these two methods compared with a six methods that used in previous researches.The goal of the paper is twofold; using Deviance information criterion and IWAK-means clustering algorithm based on SHMM.

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Face Recognition by Multi-resolution Curvelet Transform on Bit Quantized Facial Images

Cited by 6 publications

References 10 publications

Pseudo-Fisherface method for single image per person face recognition

Pseudo-Fisherface method for single image per person face recognition

A Novel Facial Expression Recognition Based on the Curvelet Features

DIC Structural HMM based IWAK-means to Enclosed Face Data

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