Using artificial neural network for human age estimation based on facial images

Kohail, Sarah

doi:10.1109/innovations.2012.6207735

Cited by 24 publications

(8 citation statements)

References 11 publications

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“…By adopting this qualitative ability, this makes it decidedly fit to be applied on issues where no relationship can be established between the inputs and the output. The ANN techniques stand as very convenient in application domains to explain extremely nonlinear phenomena [14]. Because of its high rate of learning capabilities and plasticity, it has proved to be fit to be successfully executed within a wide range of applications [25,29].…”

Section: Fig 1 All Gases Emitted From One Person (198 Gas)mentioning

confidence: 99%

Odour Based Human Identification and Classification using Neural Networks

2019

IJRTE

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Biometrics permits an individual to be authenticated and identified by computer systems following on a set of verifiable and identifiable data that are precise and unique in nature. This mechanism constitutes a cutting-edge method of identifying an individual since it precisely establishes more explicit and direct connection with humans than mere passwords since biometrics tend to use measurable behavioral and physiological characteristics of human. In this paper, a framework for human identification is proposed distinctively based on specific human odour features. 15 samples of female and male human odour are collected from different age groups, only 15 effective Volatile Organic Compounds (VOCs) are chosen. In this paper, several diverse functions of neural network activation are tested such as Levenberg-Marquardt backpropagation, Gradient descent backpropagation, and Resilient backpropagation. Besides, numerous neural network topologies are tested by means of variety hidden layers and different number of neurons and. Different energy functions were tested TAN- Sigmoid transfer, Linear transfer, and LOG- Sigmoid transfer. Considering the obtained results, employing two hidden layers with more neurons in the hidden layers- to be specific: 15 neurons in every layer- has yielded better accuracy in performance with an accuracy rate of 100%. The unsurpassed framework for algorithm learning to be used for human identification can be backpropagation learning algorithm named the Levenberg-Marquardt. The best function for activation established in this paper is the function of TANSigmoid transfer. The performance accuracy consistency in recognizing human can be enhanced using a big number of study samples.

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Section: Fig 1 All Gases Emitted From One Person (198 Gas)mentioning

confidence: 99%

Odour Based Human Identification and Classification using Neural Networks

2019

IJRTE

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“…The authors in [9] explored the application of neural networks to estimate age from real face image. A special tool developed by the authors to manually locate 94 points in each face.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Prediction of Age group from Frontal Facial Images

Abirami,

Subashini

2019

IJEAT

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Methods to automatically assess the age group of a person using his/her frontal facial image are proposed in this paper. This work is done for three major ethnicities: African, American and Asian with five different age-groups such as (1-10 years), (11-30 years), (31-50years), (51-70 years), (71-100 years). The performances of the classifiers were tested with face images of African, American and Asian population belonging to both genders. For this, first the facial parts such as the left eye, right eye, nose, mouth etc., are detected using the well-known Viola Jones Object Detection technique.450 sample images of the FERET database were considered for this study. Histogram of Gradient (HoG) and face-structure features are extracted and modeled using ANN and SVM. The efficiency of the proposed methods was tested with the facial images of various races belonging to different age-group and gender. Artificial neural network gave an accuracy of 92.10% whereas support vector machine gave an improved accuracy of 94.60%.

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“…Facial aging is a problem in face recognition, because simulating the appearance of a person across years may help recognizing his or her face [10][11]. Most of the existing facial age estimation methods usually employ handcrafted feature descriptors like Local Binary Pattern (LBP), color moments, etc., for face representation, which require strong prior knowledge [12][13]. Also, a few attempts like preprocessing is performed on learning-based feature representation in facial age estimation, which learn discriminative features directly from raw pixels.…”

Section: Introductionmentioning

confidence: 99%

Overlapped Semantic Age Group Estimation Using Hybrid PCA and Log Gabor Filter

Gangadharaiah¹,

Suresh²

2018

IJIES

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Automatic extraction of soft biometric characteristics of face image is an emerging research field in recent years. Among these soft biometrics, age estimation is very useful for several applications, like video surveillance, business intelligence, and search optimization in large databases. Generally, facial aging effects perceived in two main forms like, growth related transformations and textural variation. So, in order to generate an effective age classifier, both dimension and texture information should be used together. In this work, an age estimation system that is combination of dimension feature (Hybrid Principal Component Analysis (HPCA)) and textural feature (Log Gabor filter) for feature extraction, is used. The extracted feature values are classified by employing a multi-objective classifier named as Multi-Support Vector Machine (M-SVM). The result of the experiment shows that the proposed approach delivers a better age estimation rate on overlapping age group classes.

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Using artificial neural network for human age estimation based on facial images

Cited by 24 publications

References 11 publications

Odour Based Human Identification and Classification using Neural Networks

Odour Based Human Identification and Classification using Neural Networks

Automatic Prediction of Age group from Frontal Facial Images

Overlapped Semantic Age Group Estimation Using Hybrid PCA and Log Gabor Filter

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