Local Binary Patterns for Gender Classification

Gudla, Balakrishna; Chalamala, Srinivasa Rao; Jami, Santosh Kumar

doi:10.1109/aims.2015.13

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Theseauthorsofthispaperusedaxiomaticdesigntheory(ADT)indevelopingtheclassification model.Thismodelisusedforin-piperobotsorroboticmechanismsasillustratedin (Kharya,2012) Biologicalclassificationusingbiometricfeatureshadobtainedveryvastattentionamongresearch communityintherecentyears.Amongthese,oneofthewell-knownclassificationapplicationsis theGenderclassification.Forthisapplication,facialfeaturesofthetargetaudiencesareconsidered and are an evolving issue (Gudla & Chalamala & Jami, 2016). This is done using a texture of facialimagesratherthanusingothertraditionalbutordinaryfacialfeatures.…”

Section: Literature Surveymentioning

confidence: 99%

Comparative Performance Analysis of Various Classifiers for Cloud E-Health Users

MuthamilSelvan

Balamurugan

2019

International Journal of E-Health and Medical Communications

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Several classifiers are prevalent which act as a major drive for almost all supervised machine learning applications. These classifiers, though their objective working looks similar, they vary drastically in their performances. Some of the important factors that cause such variations are the scalability of the dataset, dataset nature, training time estimation, classifying time for the test data, prediction accuracy and the error rate computation. This paper focuses mainly on analyzing the performance of the existing four main classifiers: IF-THEN rule, C4.5 decision trees, naïve Bayes, and SVM classifier. The objective of this research article is to provide the complete statistical performance estimates of the four classifiers to the authenticated cloud users. These users have the access facility in obtaining the essential statistical information about the classifiers in study from the cloud server. Such statistical information might be helpful in choosing the best classifier for their personal or organizational benefits. The classifiers follow the traditional underlying algorithms for classification that is performed in the cloud server. These classifiers are tested on three different datasets namely PIMA, breast-cancer and liver-disorders dataset for performance analysis. The performance analysis indicators used in this research article to summarize the working of the various classifiers are training time, testing time, prediction accuracy and error rate computation. The proposed comparative analysis framework can be used to analyze the performances of the classifiers with respect to any input dataset.

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Section: Literature Surveymentioning

confidence: 99%

Comparative Performance Analysis of Various Classifiers for Cloud E-Health Users

MuthamilSelvan

Balamurugan

2019

International Journal of E-Health and Medical Communications

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“…The piece that it looks for are called features. Finding rough feature matches, in roughly the similar point in two images, CNN gets the better similarities than whole-image matching Local binary pattern based classifier which can classify the facial feature well [10]. In this method compute the histogram of LBP.…”

Section: Gender Classificationmentioning

confidence: 99%

Face Detection and Gender Classification using Facial Features

Kavitha*,

Aroquiaraj

2019

IJRTE

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Face Recognition is the most important part to identifying people in biometric system. It is the most usable biometric system. This paper focuses on human face recognition by calculating the facial features present in the image and recognizing the person using features. In every face recognition system follows the preprocessing, face detection techniques. In this paper mainly focused on Face detection and gender classification. They are performed in two stages, the first stage is face detection using an enhanced viola jones algorithm and the next stage is gender classification. Input to the video or surveillance that video converted into frames. Select few best frames from the video for detecting the face, before the particular image preprocessed using PSNR. After preprocessing face detection performed, and gender classification comparative analysis done by using a neural network classifier and LBP based classifier

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“…Hence, researchers investigated different techniques to find discriminative representations of the face to ensure reliable predictions. For instance, the Local Binary Pattern (LBP) histogram [6] has been used to extract shape and texture features from the face. However, LBP-type features are limited in describing the local texture and cannot capture the global dominant texture.…”

Section: Introductionmentioning

confidence: 99%

Person Gender Classification on RGB-D Data With Self-Joint Attention

et al. 2021

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Automatic gender classification has many potential applications including automatic annotation of images, video surveillance, security, and human-computer interaction. In the last decades, many research works focused on classifying gender using the cues from 2D images of the person's frontal view. This limits their application in the real world. Also, classifying a person's gender from a different view, pose and scale are still challenging problems. RGB-D images, containing color and depth images, have more immunity to this problem than 2D images. Recent approaches using RGB-D images explored different combinations of features descriptors from depth and color images to classify the gender. These methods used lowlevel features extracted from the depth and color separately, ignoring the inter-dependency between the two modalities. In this article, we proposed a deep learning-based approach using a self-joint attention mechanism for human gender classification on RGB-D images. The proposed attention mechanism is designed to encode the inter-dependent information between the depth and color images to enhance the feature discrimination power. We benchmarked our method on a challenging gender dataset that consists of different views, poses, and scales. The presented method outperforms the state-of-art methods with an improvement in the accuracy of 5.2%, 7.5%, and 8.7% on three different test data.

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Local Binary Patterns for Gender Classification

Cited by 6 publications

References 8 publications

Comparative Performance Analysis of Various Classifiers for Cloud E-Health Users

Comparative Performance Analysis of Various Classifiers for Cloud E-Health Users

Face Detection and Gender Classification using Facial Features

Person Gender Classification on RGB-D Data With Self-Joint Attention

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