Classification of Age Groups from Facial Image Using Histograms of Oriented Gradients

Hajizadeh, Mohammad Ali; Ebrahimnezhad, Hossein

doi:10.1109/iranianmvip.2011.6121582

Cited by 32 publications

(18 citation statements)

References 10 publications

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“…This method has been successfully applied to many computer vision problems using human body or face images, such as the pedestrian detection [39], age estimation [40], face recognition [41], gender recognition [42,43]. The principle of the HOG method is that the HOG method constructs histogram features of a sub-block of an image by accumulating the strength and direction of the gradient information at every pixel inside the sub-block.…”

Section: Proposed Methods For Person Recognition Using Visible Lighmentioning

confidence: 99%

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Nguyen

Hong

Kim

et al. 2017

Sensors

437

159

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The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

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Section: Proposed Methods For Person Recognition Using Visible Lighmentioning

confidence: 99%

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Nguyen

Hong

Kim

et al. 2017

Sensors

437

159

View full text Add to dashboard Cite

show abstract

“…Hajizadeh and Ebrahimnezhad [183] represented facial features using histogram of oriented gradients (HOG) [137]. Using probabilistic neural network (PNN) to classify HOG features extracted from several regions, they achieved 87% accuracy in classifying face images into four groups.…”

Section: Age-group Estimationmentioning

confidence: 99%

Age estimation via face images: a survey

Angulu

Tapamo

Adewumi

2018

J Image Video Proc.

126

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Facial aging adversely impacts performance of face recognition and face verification and authentication using facial features. This stochastic personalized inevitable process poses dynamic theoretical and practical challenge to the computer vision and pattern recognition community. Age estimation is labeling a face image with exact real age or age group. How do humans recognize faces across ages? Do they learn the pattern or use age-invariant features? What are these age-invariant features that uniquely identify one across ages? These questions and others have attracted significant interest in the computer vision and pattern recognition research community. In this paper, we present a thorough analysis of recent research in aging and age estimation. We discuss popular algorithms used in age estimation, existing models, and how they compare with each other; we compare performance of various systems and how they are evaluated, age estimation challenges, and insights for future research.

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“…Using nearest neighbour classifiers, they carried out accuracy of eighty percentage on age-groups 10-15, 20-25, 30-35, 40-45, 50-55 and 60-65. Hajizadeh and Ebrahimnezhad [17] represented facial features with the use of Histogram of Oriented Gradients (HOG). Using probabilistic neural network (PNN) to classify HOG functions derived from multiple areas, 87 percent accuracy was achieved in the classification of face images into four groups.…”

Section: B Age Estimation Algorithmmentioning

confidence: 99%

Hybridized Feature Extraction for Facial Age Estimation using SVM

Diri¹

2019

IJRASET

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Forging of age is increasing dramatically these days, especially in areas such as military, industry, crime, processing of visa, job application, etc. One of the areas of human-PC-interaction is developing an application for the automated estimation of age using face pictures. Typically, an age estimation system consists of the extraction and classification of aging features; both of which are important to improve efficiency. For the ageing feature extraction, the hybrid features extraction, which are a mixture of Active Appearance Models (AAM), Local Binary Patterns (LBP) and Gabor filter is applied. As global facial features, the shape of human faces is extracted with AAM. The local facial features are the features of the skin textures extracted with LBP and wrinkle extracted with Gabor filter. An age classification utilizing Support Vector Machine (SVM) classifies age in groups. Eight age group class is used for the age estimation. The result is represented in Cumulative Score. The higher the Cumulative Score (CS) becomes, the age estimate becomes accurate as well. Our system is implemented using Object Oriented Programming (OOP) with the help of Open Computer Vision (OpenCV) library and private dataset from Department of Computer Science, Ignatius Ajuru University of Education (IAUE). The result shows 93% accuracy on age classification and age estimation from facial images which is better than the former method.

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Classification of Age Groups from Facial Image Using Histograms of Oriented Gradients

Cited by 32 publications

References 10 publications

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Age estimation via face images: a survey

Hybridized Feature Extraction for Facial Age Estimation using SVM

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