Automatic Age Estimation: A Survey

Mansouri, Nabila

doi:10.13053/cys-24-2-3317

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…[21] combines facial landmark points and gravity moment and builds a matrix that represents the the juvenile age range. Other features have been based on geometry, active shape, appearance [1], and relative-order information in different ages [22]. Different classifiers have been used with hand-crafted features such as Relevance Vector Machine (RVM) [23],…”

Section: Age Estimationmentioning

confidence: 99%

“…Age estimation has evolved from a carnival curiosity to an established task in computer vision [1]. It has many applications such as human-computer interaction (HCI), biometrics, age-restricted security control, video surveillance, and teacher-student differentiation in the classroom.…”

Section: Introductionmentioning

confidence: 99%

“…Current age estimation methods use raw images commonly with Convolutional Neural Networks (CNN) [1]. While there have been significant advances in developing effective CNN architectures such as AlexNet and VGG, CNNs models are commonly used for object/subject classification.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

TAA-GCN: A temporally aware Adaptive Graph Convolutional Network for age estimation

Korban

Youngs²,

Acton³

2023

Pattern Recognition

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Section: Age Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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TAA-GCN: A temporally aware Adaptive Graph Convolutional Network for age estimation

Korban

Youngs²,

Acton³

2023

Pattern Recognition

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Improving Juvenile Age Estimation Based on Facial Landmark Points and Gravity Moment

et al. 2020

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Facial age estimation is of interest due to its potential to be applied in many real-life situations. However, recent age estimation efforts do not consider juveniles. Consequently, we introduce a juvenile age detection scheme called LaGMO, which focuses on the juvenile aging cues of facial shape and appearance. LaGMO is a combination of facial landmark points and Term Frequency Inverse Gravity Moment (TF-IGM). Inspired by the formation of words from morphemes, we obtained facial appearance features comprising facial shape and wrinkle texture and represented them as terms that described the age of the face. By leveraging the implicit ordinal relationship between the frequencies of the terms in the face, TF-IGM was used to compute the weights of the terms. From these weights, we built a matrix that corresponds to the possibilities of the face belonging to the age. Next, we reduced the reference matrix according to the juvenile age range (0–17 years) and avoided the exhaustive search through the entire training set. LaGMO detects the age by the projection of an unlabeled face image onto the reference matrix; the value of the projection depicts the higher probability of the image belonging to the age. With Mean Absolute Error (MAE) of 89% on the Face and Gesture Recognition Research Network (FG-NET) dataset, our proposal demonstrated superior performance in juvenile age estimation.

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Automatic Age Estimation: A Survey

Cited by 2 publications

References 11 publications

TAA-GCN: A temporally aware Adaptive Graph Convolutional Network for age estimation

TAA-GCN: A temporally aware Adaptive Graph Convolutional Network for age estimation

Improving Juvenile Age Estimation Based on Facial Landmark Points and Gravity Moment

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