Age estimation using a hierarchical classifier based on global and local facial features

Choi, Sung Eun; Lee, Youn Joo; Lee, Sung Joo; Park, Kang Ryoung; Kim, Jaihie

doi:10.1016/j.patcog.2010.12.005

Cited by 225 publications

(180 citation statements)

References 24 publications

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“…The characteristics of facial aging include a gradual change in appearance which is not visibly apparent over a short age gap. Based on this observation, a hierarchical age estimator [4,37] is proposed for automatic age estimation. Unlike [4,37], where the whole age range is directly partitioned into multiple age groups, in this paper, we use a binary decision tree based on SVM (SVM-BDT) [38] to perform age group classification.…”

Section: Motivationmentioning

confidence: 99%

Age estimation from face images: Human vs. machine performance

Han

Otto

Jain

2013

2013 International Conference on Biometrics (ICB)

207

125

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Section: Motivationmentioning

confidence: 99%

Age estimation from face images: Human vs. machine performance

Han

Otto

Jain

2013

2013 International Conference on Biometrics (ICB)

207

125

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“…Features extracted from local neighborhoods have been used for the purpose of age estimation, for example in Yang and Ai (2007), Gunay and Nabiyev (2008) and Choi et al (2011). In Weng et al (2013), LBP histogram features are combined with principal components of BIF, shape and textural features of AAM, and PCA projection of the original image pixels.…”

Section: Related Workmentioning

confidence: 99%

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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“…To extract the features from each patch, we use Local Binary Pattern (LBP) [12]. It is a simple, efficient, and rotation-invariant approach and successfully used for age prediction to capture the skin texture details [2,15]. In our experiments, we use 8 sampling points with a radius equal to 1.…”

Section: Datasetsmentioning

confidence: 99%

“…During aging, the human face loses collagen beneath the skin leading to thinner, darker, and more leathery skin [6]. Age-induced facial wrinkles become more distinct as a result of repeated activation of facial muscles and they start to appear in different directions depending on these muscles [2]. For example, vertical wrinkles intensify between the eyebrows while horizontal wrinkles become more apparent close to the eye corners.…”

Section: Introductionmentioning

confidence: 99%

Expression-Invariant Age Estimation

Alnajar¹,

Lou²,

Álvarez³

et al. 2014

Proceedings of the British Machine Vision Conference 2014

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In this paper, we investigate and exploit the influence of facial expressions on automatic age estimation. Different from existing approaches, our method jointly learns the age and the expression by introducing a new graphical model with a latent layer between the age/expression labels and the features. This layer aims to learn the relationship between the age and the expression and captures the face changes which induce the aging and the expression appearance, and thus obtaining expression-invariant age estimation. Conducted on two age-expression datasets (FACES [4] and Lifespan [10]), our experiments illustrate the improvement in performance when the age is jointly learnt with expression in comparison to expression-independent age estimation. The age estimation error is reduced by 14.43% and 37.75% for the FACES and Lifespan datasets respectively. Furthermore, the results obtained by our graphical model, without prior-knowledge of the expressions of the tested faces, are better that the best reported ones for both datasets.

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Age estimation using a hierarchical classifier based on global and local facial features

Cited by 225 publications

References 24 publications

Age estimation from face images: Human vs. machine performance

Age estimation from face images: Human vs. machine performance

A deep analysis on age estimation

Expression-Invariant Age Estimation

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