Overlapped Semantic Age Group Estimation Using Hybrid PCA and Log Gabor Filter

Gangadharaiah, Santhosh; Suresh, H. N.

doi:10.22266/ijies2018.0430.01

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…The age classification experiment performed on the FG-NET Aging Database [6] and MORPH database [16] which are the popular databases in the face age estimation used by the research community. The FG-NET Aging database contains 1002 highresolution colour or grayscale face images from 82 subjects ranging from age 0 to 69.…”

Section: Resultsmentioning

confidence: 99%

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Fusion Based Automatic Human Age Estimation Using Local Descriptors

.¹

2019

IJIES

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This paper introduces a new age estimation method based on the fusion of local features extracted using histogram-based local texture descriptors. In this study, the age estimation performances of well-known powerful texture descriptor with improved modification Local Binary Patterns (mLBP), and Local Phase Quantization (LPQ), which have not been analysed in depth for age estimation, investigated. In addition, multi-scale and spatial texture analysis performed for all descriptors. In the spatial texture analysis, a new approach using Overlapped Blocks obtained by discrete blocks proposed to reduce the overlapped age groups between the neighbour age group. Then feature fusion performed to investigate the age estimation accuracies of different combinations of local texture descriptors. Multiple Linear Regression (MLR) used to estimate the specific age at the end of this proposed system. The results show that the age estimation accuracy of the proposed method is better when compared to previous methods on FG-NET, MORPH databases.

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Section: Resultsmentioning

confidence: 99%

“…Table 5, shows the MAE of age estimation on MORPH Database. In Table 5, the combination of mLBP and LPQ features achieves between MAE on MORPH database compared to the existing approaches [6] and [16]. For a detailed analysis of the age estimation method, calculate the MAE for each decade separately for the MORPH database.…”

Section: Resultsmentioning

confidence: 99%

Fusion Based Automatic Human Age Estimation Using Local Descriptors

.¹

2019

IJIES

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“…The modified fuzzy set filter was experimented by utilizing MATLAB (version 2017a) with 4 GB RAM, 3.0 GHz Intel i3 processor and 500 GB hard disc [29]. The modified fuzzy set filter's performance was compared with a few existing filters in order to estimate the efficiency of proposed filter.…”

Section: Methodsmentioning

confidence: 99%

Robust Gaussian Noise Detection and Removal in Color Images using Modified Fuzzy Set Filter

Suneetha¹,

Reddy

2020

Journal of Intelligent Systems

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In the data collection phase, the digital images are captured using sensors that often contaminated by noise (undesired random signal). In digital image processing task, enhancing the image quality and reducing the noise is a central process. Image denoising effectively preserves the image edges to a higher extend in the flat regions. Several adaptive filters (median filter, Gaussian filter, fuzzy filter, etc.) have been utilized to improve the smoothness of digital image, but these filters failed to preserve the image edges while removing noise. In this paper, a modified fuzzy set filter has been proposed to eliminate noise for restoring the digital image. Usually in fuzzy set filter, sixteen fuzzy rules are generated to find the noisy pixels in the digital image. In modified fuzzy set filter, a set of twenty-four fuzzy rules are generated with additional four pixel locations for determining the noisy pixels in the digital image. The additional eight fuzzy rules ease the process of finding the image pixels,whether it required averaging or not. In this scenario, the input digital images were collected from the underwater photography fish dataset. The efficiency of the modified fuzzy set filter was evaluated by varying degrees of Gaussian noise (0.01, 0.03, and 0.1 levels of Gaussian noise). For performance evaluation, Structural Similarity (SSIM), Mean Structural Similarity (MSSIM), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Universal Image Quality Index (UIQI), Peak Signal to Noise Ratio (PSNR), and Visual Information Fidelity (VIF) were used. The experimental results showed that the modified fuzzy set filter improved PSNR value up to 2-3 dB, MSSIM up to 0.12-0.03, and NMSE value up to 0.38-0.1 compared to the traditional filtering techniques.

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Hierarchical age estimation mechanism with adaBoost-based deep instance weighted fusion

Fan

Zheng

et al. 2020

Journal of Experimental & Theoretical Artificial Intelligen

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Overlapped Semantic Age Group Estimation Using Hybrid PCA and Log Gabor Filter

Cited by 3 publications

References 15 publications

Fusion Based Automatic Human Age Estimation Using Local Descriptors

Fusion Based Automatic Human Age Estimation Using Local Descriptors

Robust Gaussian Noise Detection and Removal in Color Images using Modified Fuzzy Set Filter

Hierarchical age estimation mechanism with adaBoost-based deep instance weighted fusion

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