A Face Recognition Method Based on Total Variation Minimization and Log-Gabor Filter

In practice Gabor wavelet is often applied to extract relevant features from a facial image. This wavelet is constructed using filters of multiple scales and orientations. Based on Gabor’s theory of communication, two methods are proposed to acquire initial features from 2D images that are Gabor wavelet and Log-Gabor wavelet. Theoretically the main difference between these two wavelets is Log-Gabor wavelet produces DC free filter responses, whereas Gabor filter responses retain DC components. This experimental study determines the characteristics of Gabor and Log-Gabor filters for face recognition. In the experiment, two sixth order data tensor are created; one containing the basic Gabor feature vectors and the other containing the basic Log-Gabor feature vectors. This study reveals the characteristics of the filter orientations for Gabor and Log-Gabor filters for face recognition. These two implementations show that the Gabor filter having orientation zero means oriented at 0 degree with respect to the aligned face has the highest discriminating ability, while Log-Gabor filter with orientation three means 45 degree has the highest discriminating ability. This result is consistent across three different frequencies (scales) used for this experiment. It is also observed that for both the wavelets, filters with low frequency have higher discriminating ability.

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Face recognition method based on DLPP-SIFT

Journal of Computer Applications ◽

10.3724/sp.j.1087.2012.02588 ◽

2013 ◽

Vol 32 (9) ◽

pp. 2588-2591

Author(s):

Zheng-yi LI ◽

Gui-yu FENG ◽

Long ZHAO

Keyword(s):

Face Recognition ◽

Recognition Method

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Face Recognition Method Based on Fixed and PTZ Camera Control for Moving Humans

2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN) ◽

10.1109/icufn.2019.8806046 ◽

2019 ◽

Author(s):

Seong-Hoon Kim ◽

Jin-Woo Choi

Keyword(s):

Face Recognition ◽

Recognition Method ◽

Camera Control ◽

Ptz Camera

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Relative Total Variation Structure Analysis-Based Fusion Method for Hyperspectral and LiDAR Data Classification

Remote Sensing ◽

10.3390/rs13061143 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1143

Author(s):

Yinghui Quan ◽

Yingping Tong ◽

Wei Feng ◽

Gabriel Dauphin ◽

Wenjiang Huang ◽

...

Keyword(s):

Feature Extraction ◽

Total Variation ◽

Structure Analysis ◽

Hyperspectral Image ◽

Gabor Filter ◽

Heterogeneous Data ◽

Lidar Data ◽

Fusion Method ◽

Wide Range ◽

Relative Total Variation

The fusion of the hyperspectral image (HSI) and the light detecting and ranging (LiDAR) data has a wide range of applications. This paper proposes a novel feature fusion method for urban area classification, namely the relative total variation structure analysis (RTVSA), to combine various features derived from HSI and LiDAR data. In the feature extraction stage, a variety of high-performance methods including the extended multi-attribute profile, Gabor filter, and local binary pattern are used to extract the features of the input data. The relative total variation is then applied to remove useless texture information of the processed data. Finally, nonparametric weighted feature extraction is adopted to reduce the dimensions. Random forest and convolutional neural networks are utilized to evaluate the fusion images. Experiments conducted on two urban Houston University datasets (including Houston 2012 and the training portion of Houston 2017) demonstrate that the proposed method can extract the structural correlation from heterogeneous data, withstand a noise well, and improve the land cover classification accuracy.

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