Facial expression recognition using wavelet transform and local binary pattern

Alsubari, Akram; Satange, D. N.; Ramteke, R. J.

doi:10.1109/i2ct.2017.8226147

Cited by 11 publications

(4 citation statements)

References 11 publications

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“…These will be converted to a decimal number and will be replaced with the center pixel as shown in Figure 4. In other words, the central pixel is the threshold value for its neighbor-pixels [20].…”

Section: ) Slbpmentioning

confidence: 99%

Composite Feature Extraction and Classification for Fusion of Palm-Print and Iris Biometric Traits

Alsubari

Hannan

Alzahrani

et al. 2019

Eng. Technol. Appl. Sci. Res.

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Palm-print and iris biometric traits fusion are implemented in this paper. The region of interest (ROI) of a palm is extracted by using the valley detection algorithm and the ROI of an iris is extracted based on the neighbor-pixels value algorithm (NPVA). Statistical local binary pattern (SLBP) is applied to extract the local features of palm and iris. For enhancing the palm features, a combination of histogram of oriented gradient (HOG) and discrete cosine transform (DCT) is applied. Gabor-Zernike moment is used to extract the iris features. This experimentation was carried out in two modes: verification and identification. The Euclidean distance is used in the verification system. In the identification system, the fuzzy-based classifier was proposed along with built-in classification functions in MATLAB. CASIA datasets of palm and iris were used in this research work. The proposed system accuracy was found to be satisfactory.

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Section: ) Slbpmentioning

confidence: 99%

Composite Feature Extraction and Classification for Fusion of Palm-Print and Iris Biometric Traits

Alsubari

Hannan

Alzahrani

et al. 2019

Eng. Technol. Appl. Sci. Res.

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“…The SVM is used as a classification. Akram Alsubari et al, [13] have proposed an Indian face recognition with different facial expressions. The final features are obtained using DWT and LBP.…”

Section: Nibble-based Face Recognition U Sing Convolution Of Hybrid Featuresmentioning

confidence: 99%

Nibble-Based Face Recognition Using Convolution of Hybrid Features

Rangsee

Raja

Venugopal

2019

2019 3rd International Conference on Imaging, Signal Processing and Communication (ICISPC)

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Face Recognition technique is used to identify human beings effectively without their intervention. In this paper, we propose a novel nibble-based face recognition using convolution of hybrid features. Face images are converted from color to grayscale anda decimal values of each pixel is represented using 8-bit binary. The novel technique of converting 8-bit binary pixel into Left-Side Nibble (LSN) and Right-Side Nibble (RSN) is introduced to increase computational speed. The 4-bit LSN is converted to a decimal value varying between 0 and 240. DWT is applied on LSN matrix and only LL band coefficients are considered as first set of transform domain features. The 4-bit RSN is considered and converted into decimal values ranging between 0 and 15. HOG is applied on RSN matrix to generate second set of spatial domain features. The final hybrid features are generated by performing linear convolution on spatial-and transformdomain features. The Artificial Neural Network (ANN) is used to compare and classify the database image features and test image features to compute performance parameters of proposed algorithm. It is observed that the performance of the proposed method is better than the existing methods.

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“…The Local Binary Pattern is to compute the relative between the pixels [6], [9]. So, it is compared the central pixel against the 8-neighbor as showing in the Fig.3.…”

Section: Local Binary Patternmentioning

confidence: 99%

Extraction of Face and Palmprint Features based on LBP, HOG and Zernike Moments

Alsubari¹,

Ramteke²

2017

IJCA

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This paper describes the recognition of multimodal biometric systems based on the face and palmprint features. The common feature extraction technique between the palmprint and face recognition system is the Local Binary Pattern. In the palmprint recognition, the region of palm was extracted form the entire hand. The Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) were used to extract the features of palm. The Zernike moments and LBP were used to extract the features of face. The features of palm and face were integrated as feature vector. To evaluated the accuracy of the system, different classifications were applied such as SVM, KNN, Linear Discriminant. This experiment was performed on the ORL and CASIA database. The proposed system was tested on the said database and found to be satisfactory.

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Facial expression recognition using wavelet transform and local binary pattern

Cited by 11 publications

References 11 publications

Composite Feature Extraction and Classification for Fusion of Palm-Print and Iris Biometric Traits

Composite Feature Extraction and Classification for Fusion of Palm-Print and Iris Biometric Traits

Nibble-Based Face Recognition Using Convolution of Hybrid Features

Extraction of Face and Palmprint Features based on LBP, HOG and Zernike Moments

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