Facial Expression Recognition Based on Features Derived From the Distinct LBP and GLCM

Murty, Gorti Satyanarayana; Kiran, J. Sasi; Kumar, V. Vijaya

doi:10.5815/ijigsp.2014.02.08

Cited by 16 publications

(9 citation statements)

References 17 publications

(12 reference statements)

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“…Local texture patterns of images plays an important role in a wide variety of image processing applications like image analysis, image retrieval [ 1, 2 ,3, 4], age classification [5,6], face recognition [7][8][9][10][11], texture classification [12][13][14]. The task of deriving a texture pattern is one of the difficult problems because texture is defined by many researchers based on their applications, perceptual motivation and there is no generally agreed definition for textures.…”

Section: Introductionmentioning

confidence: 99%

Texture Classification Using Complete Texton Matrix

Kumari¹,

Kumar²,

Satyanarayana³

2017

IJIGSP

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Abstract-This paper presents a complete image feature representation, based on texton theory proposed by Julesz's, called as a complete texton matrix (CTM)for texture image classification. The present descriptor can be viewed as an improved version of texton cooccurrence matrix (TCM) [1] and Multi-texton histogram (MTH) [2]. It is specially designed for natural image analysis and can achieve higher classification rate. TheCTM can express the spatial correlation of textons and can be considered as a generalized visual attribute descriptor. This paper initially quantized the original textures into 256 colors and computed color gradient from RGB vector space. Then the statistical information of eleven derived textons, on a 2 x 2 grid in a nonoverlapped manner are computed to describe image features more precisely. To reduce the dimensionality the present paper extended the concept of present descriptor and derived a compact CTM (CCTM). The proposed CTM and CCTM methods are extensively tested on the Brodtaz, Outex and UIUC natural images. The results demonstrate the superiority of the present descriptor over the state-of-art representative schemes such as uniform LBP (ULBP), local ternary pattern (LTP), complete -LBP (CLBP), TCM and MTH.

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Section: Introductionmentioning

confidence: 99%

Texture Classification Using Complete Texton Matrix

Kumari¹,

Kumar²,

Satyanarayana³

2017

IJIGSP

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“…The local binary pattern is used as a tool to capture facial expression in an image [11]. The local binary pattern played an important role to convey the required facial expression information.…”

Section: Literature Surveymentioning

confidence: 99%

An Interactive Approach for Retrieval of Semantically Significant Images

Mane¹,

Rathi²,

Bawane³

2016

IJIGSP

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Abstract-Content-based image retrieval is the process of recovering the images that are based on their primitive features such as texture, color, shape etc. The main challenge in this type of retrieval is the gap between lowlevel primitive features and high-level semantic concepts. This is known as the semantic gap. This paper proposes an interactive approach for optimizing the semantic gap. The primitive features used are HSV histogram, local binary pattern histogram, and color coherence vector histogram. The mapping between primitive features of the image and its semantic concepts is done by involving the user in the feedback loop. Proposed primitive feature extraction method shows improved image retrieval results (Average precision 73.1%) over existing methods. We have proposed an innovative relevance feedback technique in which the concept of prominent features is introduced. On the application of the relevance feedback, only prominent features which are having maximum similarity are utilized. This method reduces the feature length and increases the efficiency. Our own interactive approach for relevance feedback is not only computationally simple and fast but also shows improvement in the retrieval of semantically meaningful relevant images as we go on increasing the iterations.Index Terms-Semantic gap, content-based image retrieval, relevance feedback, HSV histogram, local binary pattern, color coherence vector.

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“…So many approaches are available for age group classification based on pattern approach [11][12][13][14][15] According to Ojala etal [1] LBP, which generates 256 patterns are grouped in to 59 uniform and 197 non uniform LBP patterns (NULBP). Many researchers have considered only uniform patterns (ULBP) for texture classifications due to their small numbers and claiming that most of the textures are dominated by only uniform LBP's.…”

Section: A Age Classification Based On Significant Local Maximum Edgmentioning

confidence: 99%

Novel Approach for Child and Adulthood Classification Based on Significant Prominent Binary Patterns of Local Maximum Edge (SPBPLME)

Babu¹,

E²,

B³

2015

IJITCS

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Abstract-This paper derives a new procedure for age classification of facial image based on the local region of facial image. The local region of facial image is extracted from a Significant Binary Pattern of Local Maximum Edge (SBPLME). The SBPLME is generated by calculating the absolute value of local difference between the average of local 3×3 sub window pixel values and its neighbors instead of the center pixel value. In the case of Local Maximum Edge Binary Pattern (LMEBP) calculating the absolute value of local difference between the center pixel value of local 3×3 sub window and its neighbors. The proposed SBPLME can generate 512 (0 to 511) different patterns. The present paper utilized Prominent LBP (PLBP) on the proposed SBPLME. The PLBP contains the significant patterns of Uniform LBP (ULBP) and Non Uniform LBP (NULBP). Thus the derived Significant PLBP of Local Maximum Edge (SPBPLME) becomes an efficient image classification and analysis, which will have a significant role in many areas. The novelty of the proposed SPBPLME method is, it has shown excellent age classification results by reducing the overall dimension, thus reducing the overall complexity.

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Facial Expression Recognition Based on Features Derived From the Distinct LBP and GLCM

Cited by 16 publications

References 17 publications

Texture Classification Using Complete Texton Matrix

Texture Classification Using Complete Texton Matrix

An Interactive Approach for Retrieval of Semantically Significant Images

Novel Approach for Child and Adulthood Classification Based on Significant Prominent Binary Patterns of Local Maximum Edge (SPBPLME)

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