Age Classification Based on Features Extracted From Third Order Neighborhood Local Binary Pattern

Kumar, Pullela S V V S R; P, Kiran Kumar Reddy; Kumar, V. Vijaya

doi:10.21917/ijivp.2014.0136

Cited by 5 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…In recent decades, many studies have been established for automatic estimation of gender from biometric data. Various modalities have been explored in the literature for age and gender recognition, such as behavioral traits, speech [3][4][5][6] and gait [7,8] or physiological traits such as face [9][10][11], iris [12,13], fingerprint [14][15][16], skin [17] and hand veins [18][19][20][21][22]. Nowadays, the COVID-19 pandemic imposes the use of a contactless biometric systems to prevent the spread of the contagious disease efficiently.…”

Section: Introductionmentioning

confidence: 99%

Pyramid histograms of oriented gradient for age and gender recognition using finger veins

Damak

Boukhris

Damak

et al. 2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

View full text Add to dashboard Cite

Nowadays, the COVID-19 pandemic imposes the use of a contactless biometric system to prevent the spread of contagious diseases efficiently. Hand veins are contactless and independent of the body's appearance. However, researches on age and gender estimation by hand veins are very limited. They focused only on age group discrimination and not the exact age. Estimating the age and gender by veins features is a challenging task since hand vein images are poor in quality and subject to variation in illumination. In this paper, a finger vein gender and age recognition system based on Pyramidal histograms of oriented gradient (PHOG) is presented. PHOG can better describe both the local shape and the spatial distribution of the veins as the image is divided into sub-regions at different resolutions for which the HOG descriptor is applied. Experimental validation on finger vein databases MMCBNU 6000 and UTFVP demonstrates the effectiveness of extracted features in gender classification and age estimation including ages from 16 to 72 years with an uncertainty of one year. The middle finger of the left hand provides the best results for both age and gender classification (F-measure 100%) for MMCBNU 6000 database, whereas for UTFVP database, F-measure is about 98,62% for age estimation and 99,47% for gender classification. A comparison study with recent approaches is carried on, showing an improvement of F-measure by 5.76% for age estimation and 1.38% for gender classification.

show abstract

“…In recent years the texture features are extracted locally and these methods focuses on extraction of pattern based features. The local binary pattern (LBP) [7] and its variants played a major role in extracting local features and LBP based methods are used in many applications like face detection [8,9], texture classification [9,10], content based image retrieval (CBIR) [11,12], age classification [13,14], motion detection [15] and background subtraction [16]. The LBP based methods encode micro level information of edges, local features, spots around each pixel of the neighborhood based on intensity information.…”

Section: Introductionmentioning

confidence: 99%

Local Gradient Dual Coding Book (LGDCB) Framework for Effective Texture Classification

K.S.R.K¹

2019

IJATCSE

View full text Add to dashboard Cite

Local descriptors are popular in texture classification and they are robust and sustain and perform well in varying pose, lighting and illumination condition. The accuracy of these local descriptors depends up on the precision of local features derived on the local neighborhood. To achieve robustness this paper initially computes eight directional edge responses using kirsch masks for each sampling point of 3x3 overlapping window. This paper divides the 3x3 edge responses( E r ) windows into a dual matrix consist of four sampling pixels each. On each matrix this paper computes the edge response ranks and based on this a coding book sequence number is generated for each matrix and this process derives dual edge response sequence matrix (DE r SM). The co-occurrence matrix and grey level cooccurrence matrix (GLCM) features derived on DE r SM represent the feature vector. The proposed descriptor is tested with well-known texture databases with different categories. The experimental results of the proposed descriptor are compared with state-of-art local descriptors and the results demonstrate the efficacy of the proposed method over the other methods.

show abstract

Age Classification Based on Features Extracted From Third Order Neighborhood Local Binary Pattern

Cited by 5 publications

References 16 publications

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

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

Pyramid histograms of oriented gradient for age and gender recognition using finger veins

Local Gradient Dual Coding Book (LGDCB) Framework for Effective Texture Classification

Contact Info

Product

Resources

About