Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face Recognition

Yang, Meng; Zhang, Lei; Shiu, Simon; Zhang, David

doi:10.1109/tifs.2012.2217332

Cited by 96 publications

(50 citation statements)

References 48 publications

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“…The ability of 2D Gabor filter to clearly describe the local characteristics of both high-and low-frequencies in the imagery has facilitated its successful application in high-resolution remote sensing imagery segmentation [26,29]. An even-symmetric linear Gabor filter has the following form [30]:…”

Section: Feature Extractionmentioning

confidence: 99%

Dynamic Post-Earthquake Image Segmentation with an Adaptive Spectral-Spatial Descriptor

Sun

Hao²,

Chen³

et al. 2017

Remote Sensing

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Abstract:The region merging algorithm is a widely used segmentation technique for very high resolution (VHR) remote sensing images. However, the segmentation of post-earthquake VHR images is more difficult due to the complexity of these images, especially high intra-class and low inter-class variability among damage objects. Herein two key issues must be resolved: the first is to find an appropriate descriptor to measure the similarity of two adjacent regions since they exhibit high complexity among the diverse damage objects, such as landslides, debris flow, and collapsed buildings. The other is how to solve over-segmentation and under-segmentation problems, which are commonly encountered with conventional merging strategies due to their strong dependence on local information. To tackle these two issues, an adaptive dynamic region merging approach (ADRM) is introduced, which combines an adaptive spectral-spatial descriptor and a dynamic merging strategy to adapt to the changes of merging regions for successfully detecting objects scattered globally in a post-earthquake image. In the new descriptor, the spectral similarity and spatial similarity of any two adjacent regions are automatically combined to measure their similarity. Accordingly, the new descriptor offers adaptive semantic descriptions for geo-objects and thus is capable of characterizing different damage objects. Besides, in the dynamic region merging strategy, the adaptive spectral-spatial descriptor is embedded in the defined testing order and combined with graph models to construct a dynamic merging strategy. The new strategy can find the global optimal merging order and ensures that the most similar regions are merged at first. With combination of the two strategies, ADRM can identify spatially scattered objects and alleviates the phenomenon of over-segmentation and under-segmentation. The performance of ADRM has been evaluated by comparing with four state-of-the-art segmentation methods, including the fractal net evolution approach (FNEA, as implemented in the eCognition software, Trimble Inc., Westminster, CO, USA), the J-value segmentation (JSEG) method, the graph-based segmentation (GSEG) method, and the statistical region merging (SRM) approach. The experiments were conducted on six VHR subarea images captured by RGB sensors mounted on aerial platforms, which were acquired after the 2008 Wenchuan Ms 8.0 earthquake. Quantitative and qualitative assessments demonstrated that the proposed method offers high feasibility and improved accuracy in the segmentation of post-earthquake VHR aerial images.

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Section: Feature Extractionmentioning

confidence: 99%

Dynamic Post-Earthquake Image Segmentation with an Adaptive Spectral-Spatial Descriptor

Sun

Hao²,

Chen³

et al. 2017

Remote Sensing

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“…Recently, the combination of a Riesz-based method and LBP has been shown to be an effective way for many applications [16,24,33]. Moreover, Lei et al [19] developed an approach to exploit the neighbouring relationship in the spatial domain and various Gabor faces.…”

Section: Riesz-based Volume Local Binary Patterns (Rvlbp)mentioning

confidence: 99%

“…Recent studies [9,16,24,33,35,36] have demonstrated the local image information can be well characterized in a unified theoretic framework, namely the Riesz transform. Felsberg and Sommer proposed to use the Riesz transform for image processing [9].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, a monogenic signal can well address the limitations of Gabor filters (i.e., not optimal and the restricted maximum bandwidth) because of the utilization of a log-Gabor filter. Until recently, Riesz transform has attracted much interest from researchers in the field for texture classification [36] and face analysis [16,33]. However, from the intrinsic dimension theorem [34], the 1st-order Riesz transform is designed just for an intrinsic 1D signal, but losing some important complex structures, such as corners.…”

Section: Introductionmentioning

confidence: 99%

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Riesz-based Volume Local Binary Pattern and A Novel Group Expression Model for Group Happiness Intensity Analysis

Huang¹,

Dhall²,

Zhao³

et al. 2015

Procedings of the British Machine Vision Conference 2015

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Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a group of people. For group emotional intensity analysis, feature extraction and group expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a group of people in an image. Firstly, we combine the Riesz transform and the local binary pattern descriptor, named Riesz-based volume local binary pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new group expression model, which considers global and local attributes. Intensive experiments are performed on three challenging facial expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new group expression model with the new feature. Our experimental results demonstrate the promising performance for group happiness intensity analysis.

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“…In order to achieve high recognition accuracy the extracted features are fused. The experimental results show that MBC based feature extraction method achieves high accuracy with lower time and space complexity [8].…”

Section: Introductionmentioning

confidence: 98%

Facial Expression Recognition Using Improved Artificial Bee Colony Algorithm

Saravanan¹

2017

ijetst

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:In recent year, Emotion recognition can be done through different modalities, such as speech, facial expression, body gesture etc. Emotion recognition through facial expression has attracted a lot of interest. Generally the emotions are classified as anger, disgust, fear, happiness, sadness, and surprise. The accurate prediction of facial expression is difficult due to the illumination, pose facial occlusions and different shape. To solve this problem the previous system introduced salient facial patches based facial expression recognition. However it has lower true positive rate. In order to improve the face recognition accuracy, the proposed system introduced Improved Artificial Bee Colony algorithm (IABC) based facial expression recognition. In this proposed work, the face image is detected by Viola-Jones approach. By using low pass filter the noises in the images are removed. Then the active facial patches with respect to the position of eyes, eyebrows, nose, and lip corner are extracted from preprocessed images. In order to obtain salient patches, the active patches are further processed. After that an optimal features are selected by using IABC algorithm. Finally the different facial expression anger, disgust, fear, happiness, sadness, and surprise are classified by using multiclass Support Vector Machine (MSVM). The experimental results show that the proposed system achieves better performance compared with the existing system in terms of accuracy, precision and recall.

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Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face Recognition

Cited by 96 publications

References 48 publications

Dynamic Post-Earthquake Image Segmentation with an Adaptive Spectral-Spatial Descriptor

Dynamic Post-Earthquake Image Segmentation with an Adaptive Spectral-Spatial Descriptor

Riesz-based Volume Local Binary Pattern and A Novel Group Expression Model for Group Happiness Intensity Analysis

Facial Expression Recognition Using Improved Artificial Bee Colony Algorithm

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