Human age estimation using bio-inspired features

Guo, Guodong; Mu, Guowang; Fu, Yun; Huang, Thomas S.

doi:10.1109/cvpr.2009.5206681

Cited by 448 publications

(420 citation statements)

References 24 publications

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“…With regards to the learning algorithm, several approaches have been proposed, including, among others, Support Vector Machines / Regressors (Guo et al (2009);Han et al (2013);Chang et al (2011);Weng et al (2013)), neural networks (Lanitis et al (2004)) and their variant of Conditional Probability Neural Network (Geng et al (2013)), Random Forests (Montillo and Ling (2009)), and projection techniques such as Partial Least Squares (PLS) and Canonical Correlation Analysis (CCA), along with their regularized and kernelized versions (Guo and Mu (2011). An extensive comparison of these classification schemes for age estimation has been reported ; Huerta et al (2014)), and the advantageousness of CCA was demonstrated over others, both regarding accuracy and efficiency.…”

Section: Related Workmentioning

confidence: 99%

“…Later, a pooling step downscales the results with a non-linear reduction, typically a MAX or STD operation, progressively encoding the results into a vector signature. In Guo et al (2009), the authors carefully design a two-layer simplification of this model for age estimation by manually setting the number of bands and orientations for convolution and pooling. Such features are also used in their posterior works, e.g.…”

Section: Related Workmentioning

confidence: 99%

“…The most typical ones consist of either hand-tuned multi-level filter banks (Guo et al (2009) ;Geng et al (2013); Han et al (2013)), that intend to emulate the behavior of primary visual cortex cells, or fine-grained facial meshes to accomplish precise alignment through dozens of facial landmarks (Chang et al (2011);Geng et al (2007); Lanitis et al (2004)). In any case, the resulting extraction schemes are difficult to replicate, and the high-dimensional visual descriptors in many cases take considerable time to be computed.…”

Section: Introductionmentioning

confidence: 99%

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A deep analysis on age estimation

Huerta

Fernández²,

Segura³

et al. 2015

Pattern Recognition Letters

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The automatic estimation of age from face images is increasingly gaining attention, as it facilitates applications including advanced video surveillance, demographic statistics collection, customer profiling, or search optimization in large databases. Nevertheless, it becomes challenging to estimate age from uncontrollable environments, with insufficient and incomplete training data, dealing with strong person-specificity and high within-range variance. These difficulties have been recently addressed with complex and strongly hand-crafted descriptors, difficult to replicate and compare. This paper presents two novel approaches: first, a simple yet effective fusion of descriptors based on texture and local appearance; and second, a deep learning scheme for accurate age estimation. These methods have been evaluated under a diversity of settings, and the extensive experiments carried out on two large databases (MORPH and FRGC) demonstrate state-of-the-art results over previous work.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A deep analysis on age estimation

Huerta

Fernández²,

Segura³

et al. 2015

Pattern Recognition Letters

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show abstract

“…Therefore, each speech recording is represented by a matrix X 1 ∈ R 60×128 + . BIFs are extracted from each face image following the procedure for human age estimation proposed in [5]. These features are actually a pyramid of Gabor filters and are similar to the way the human visual system processes visual stimuli.…”

Section: A Feature Extractionmentioning

confidence: 99%

“…This approach initially performs age group classification and subsequently, Support Vector Regression (SVR) is applied to predict the final age. Biologically inspired features (BIFs) based on Gabor filters are deployed for age estimation in [5]. In [6], a framework for age estimation via face image analysis is proposed that includes face detection, discriminative manifold learning, and multiple linear regression.…”

Section: Introductionmentioning

confidence: 99%

Multi-way regression for age prediction exploiting speech and face image information

Pantraki

Kotropoulos

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, the problem of age estimation is addressed based on two modalities: speech utterances and speakers' face images. The proposed age estimation framework employs the Shifted Covariates REgression Analysis for Multiway data (SCREAM) model, which combines Parallel Factor Analysis 2 and Principal Covariates Regression. SCREAM is able to extract a few latent variables from multi-way data and compute regression coefficients. Initially, biologically inspired features are extracted from speech utterances and face images and are suitable feature matrices are created to be fed to the multi-way SCREAM model. For bimodal age estimation, the visual and aural features are appropriately combined in a single matrix for each person. Experimental results demonstrate the profit of combining the two modalities. The performance admitted by the multi-way regression for age estimation is also measured on the benchmark face image dataset FG-NET. The proposed method is found to be competitive to state-of-the-art age estimation methods.

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Giant panda age recognition based on a facial image deep learning system

Hou

et al. 2022

Ecology and Evolution

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The conservation of the giant panda ( Ailuropoda melanoleuca ), as an iconic vulnerable species, has received great attention in the past few decades. As an important part of the giant panda population survey, the age distribution of giant pandas can not only provide useful instruction but also verify the effectiveness of conservation measures. The current methods for determining the age groups of giant pandas are mainly based on the size and length of giant panda feces and the bite value of intact bamboo in the feces, or in the case of a skeleton, through the wear of molars and the growth line of teeth. These methods have certain flaws that limit their applications. In this study, we developed a deep learning method to study age group classification based on facial images of captive giant pandas and achieved an accuracy of 85.99% on EfficientNet. The experimental results show that the faces of giant pandas contain some age information, which mainly concentrated between the eyes of giant pandas. In addition, the results also indicate that it is feasible to identify the age groups of giant pandas through the analysis of facial images.

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Human age estimation using bio-inspired features

Cited by 448 publications

References 24 publications

A deep analysis on age estimation

A deep analysis on age estimation

Multi-way regression for age prediction exploiting speech and face image information

Giant panda age recognition based on a facial image deep learning system

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