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Journal Article

Age Estimation Using Classifier Artificial Neural Network and Support Vector Machine Based On Face Images.

TL;DR: The propose approach exploits scattering transform gives more information about features of the facial images, and results for face based age estimation obtain by artificial neural network is more effective than support vector machine.
Abstract: The most prominent challenge in the facial age estimation is lack of sufficient and incomplete training data. Aging is slower and gradual process therefore faces near close ages look quite similar this can allows us to utilize the face images at neighbouring ages with modelling to particular age. There are many potential applications in age specific human computer interaction for security control and surveillance monitoring. In the last few years biologically inspired features are used for human age estimation for face images but recently more focus put on method like scattering transform. The propose approach exploits scattering transform gives more information about features of the facial images. An efficient descriptor consisting scattering transform which scatters the gabor coefficients and pulled with Gaussian smoothing in multiple layer and is evaluated for facial feature extraction. These extracted features are classified using support vector machine and artificial neural network. Results for face based age estimation obtain by artificial neural network is more effective than support vector machine.

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Citations
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Book ChapterDOI
01 Jan 2020
TL;DR: In this article, a review of the related research work in the field of feature extraction methodologies viz MFCC (Mel Frequency Cepstral Coefficient), LPC (Linear Predictive Coding), Wavelet, DWT (Discrete Wavelet Transform) and PLP (Perceptual Linear Predictive) etc.
Abstract: Current technology development in the field of artificial intelligence and IoT has resulted in increased importance to research in speech processing. Researchers are emphasizing on speech processing and its applications due to increased acceptance of technology based on AI and IoT. Natural voice or speech signal available needs to be digitized for age in processing and feature extraction. Speech signal consist of scads of information categorized broadly as gender based, voice characteristics based, emotion based, speaker based etc. Recognizing the importance of feature extraction and classification for speech processing in various applications, significant research has been carried out for various methodologies related to diversified applications. This manuscript attempts to study and review the related research work in the field of feature extraction methodologies viz MFCC (Mel Frequency Cepstral Coefficient), LPC (Linear Predictive Coding), Wavelet, DWT (Discrete Wavelet Transform) and PLP (Perceptual Linear Predictive) etc. Researchers have also given importance to classifiers like SVM (Support Vector Machine), ANN (Artificial Neural Network), GMM (Gaussian Mixture model), HMM (Hidden Markov model) etc. The comparison of these classifiers has been presented in this review. The prime objective of this review paper is to observe the relationship between the variance of speech parameters, feature extraction methodologies and classifiers. The endeavor of this review is to establish the comparative observation which shall help the budding researchers for selection of feature extraction technique as well as classifier for various speech processing application considering specific advantages and disadvantages.

1 citations

References
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Journal ArticleDOI
TL;DR: In this paper, the effects of aging on facial appearance can be explained using learned age transformations and present experimental results to show that reasonably accurate estimates of age can be made for unseen images.
Abstract: The process of aging causes significant alterations in the facial appearance of individuals When compared with other sources of variation in face images, appearance variation due to aging displays some unique characteristics Changes in facial appearance due to aging can even affect discriminatory facial features, resulting in deterioration of the ability of humans and machines to identify aged individuals We describe how the effects of aging on facial appearance can be explained using learned age transformations and present experimental results to show that reasonably accurate estimates of age can be made for unseen images We also show that we can improve our results by taking into account the fact that different individuals age in different ways and by considering the effect of lifestyle Our proposed framework can be used for simulating aging effects on new face images in order to predict how an individual might look like in the future or how he/she used to look in the past The methodology presented has also been used for designing a face recognition system, robust to aging variation In this context, the perceived age of the subjects in the training and test images is normalized before the training and classification procedure so that aging variation is eliminated Experimental results demonstrate that, when age normalization is used, the performance of our face recognition system can be improved

933 citations

Journal ArticleDOI
TL;DR: The age manifold learning scheme for extracting face aging features is introduced and a locally adjusted robust regressor for learning and prediction of human ages is designed, which improves the age estimation accuracy significantly over all previous methods.
Abstract: Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human ages. The aging process is determined by not only the person's gene, but also many external factors, such as health, living style, living location, and weather conditions. Males and females may also age differently. The current age estimation performance is still not good enough for practical use and more effort has to be put into this research direction. In this paper, we introduce the age manifold learning scheme for extracting face aging features and design a locally adjusted robust regressor for learning and prediction of human ages. The novel approach improves the age estimation accuracy significantly over all previous methods. The merit of the proposed approaches for image-based age estimation is shown by extensive experiments on a large internal age database and the public available FG-NET database.

661 citations

Journal ArticleDOI
01 Feb 2004
TL;DR: The aim of this work is to design classifiers that accept the model-based representation of unseen images and produce an estimate of the age of the person in the corresponding face image, which indicates that machines can estimate theAge of a person almost as reliably as humans.
Abstract: We describe a quantitative evaluation of the performance of different classifiers in the task of automatic age estimation. In this context, we generate a statistical model of facial appearance, which is subsequently used as the basis for obtaining a compact parametric description of face images. The aim of our work is to design classifiers that accept the model-based representation of unseen images and produce an estimate of the age of the person in the corresponding face image. For this application, we have tested different classifiers: a classifier based on the use of quadratic functions for modeling the relationship between face model parameters and age, a shortest distance classifier, and artificial neural network based classifiers. We also describe variations to the basic method where we use age-specific and/or appearance specific age estimation methods. In this context, we use age estimation classifiers for each age group and/or classifiers for different clusters of subjects within our training set. In those cases, part of the classification procedure is devoted to choosing the most appropriate classifier for the subject/age range in question, so that more accurate age estimates can be obtained. We also present comparative results concerning the performance of humans and computers in the task of age estimation. Our results indicate that machines can estimate the age of a person almost as reliably as humans.

610 citations

Proceedings ArticleDOI
Xin Geng1, Zhi-Hua Zhou2, Yu Zhang2, Gang Li1, Honghua Dai1 
23 Oct 2006
TL;DR: The AGES (AGing pattErn Subspace) method for automatic age estimation is proposed, which aims to model the aging pattern, which is defined as a sequence of personal aging face images, by learning a representative subspace.
Abstract: Age Specific Human-Computer Interaction (ASHCI) has vast potential applications in daily life. However, automatic age estimation technique is still underdeveloped. One of the main reasons is that the aging effects on human faces present several unique characteristics which make age estimation a challenging task that requires non-standard classification approaches. According to the speciality of the facial aging effects, this paper proposes the AGES (AGing pattErn Subspace) method for automatic age estimation. The basic idea is to model the aging pattern, which is defined as a sequence of personal aging face images, by learning a representative subspace. The proper aging pattern for an unseen face image is then determined by the projection in the subspace that can best reconstruct the face image, while the position of the face image in that aging pattern will indicate its age. The AGES method has shown encouraging performance in the comparative experiments either as an age estimator or as an age range estimator.

306 citations


"Age Estimation Using Classifier Art..." refers background in this paper

  • ...Dai [4] say, active appearance model consist face images in a sequence of age ascending for the same person....

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