Automatic age classification with LBP

Günay, Asuman; Nabiyev, Vasif V.

doi:10.1109/iscis.2008.4717926

Cited by 133 publications

(92 citation statements)

References 11 publications

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“…The first attempts in this research line were in [72,73] where texture and shape features were jointly exploited to increase the descriptor robustness in order to estimate the human ages through a multiple-group classification scheme with 5-year intervals taking also advantage from the gender knowledge (since the aging patterns are different for males and females). Successively, the interest in appearance-based descriptors arose exponentially and LBP was used for appearance features extraction in an automatic age estimation system proposed in [74], whereas some variants were proposed and tested in [75,76]. Gabor features were also tried for age estimation purposes [77] demonstrating their discriminative power.…”

Section: Agementioning

confidence: 99%

A study on different experimental configurations for age, race, and gender estimation problems

Carcagnì

Coco

Cazzato

et al. 2015

J Image Video Proc.

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This paper presents a detailed study about different algorithmic configurations for estimating soft biometric traits. In particular, a recently introduced common framework is the starting point of the study: it includes an initial facial detection, the subsequent facial traits description, the data reduction step, and the final classification step. The algorithmic configurations are featured by different descriptors and different strategies to build the training dataset and to scale the data in input to the classifier. Experimental proofs have been carried out on both publicly available datasets and image sequences specifically acquired in order to evaluate the performance even under real-world conditions, i.e., in the presence of scaling and rotation.

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Section: Agementioning

confidence: 99%

A study on different experimental configurations for age, race, and gender estimation problems

Carcagnì

Coco

Cazzato

et al. 2015

J Image Video Proc.

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show abstract

“…LBP is used to describe texture for face recognition, gender classification, age estimation, face detection, and face and facial component tracking. Gunay and Nabiyev [94] used LBP to characterize texture features for age estimation. They reported accuracy of 80% on FERET [77] dataset using nearest neighbor classifier and 80-90% accuracy on FERET and PIE datasets using AdaBoost classifier [78].…”

Section: Local Binary Patternsmentioning

confidence: 99%

Age estimation via face images: a survey

Angulu

Tapamo

Adewumi

2018

J Image Video Proc.

128

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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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“…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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Automatic age classification with LBP

Cited by 133 publications

References 11 publications

A study on different experimental configurations for age, race, and gender estimation problems

A study on different experimental configurations for age, race, and gender estimation problems

Age estimation via face images: a survey

A deep analysis on age estimation

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