Abstract
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.
Highlights
Age estimation enables the automatic tagging of a person’s age with a specific number or age bracket.It is relevant in real-world applications such as web access control [1], criminal investigations [2], forensics [3] and healthcare [4], where it can be useful for addressing the problem of estimating the age of a separated or unaccompanied child [5].Age estimation systems require inputs of features such as the iris [6], voice [1], teeth [7] or blood [8]
Contributions: In this paper, we present the first-time use of the combination of facial landmark points and Term Frequency Inverse Gravity Moment (TF-IGM) to contribute a juvenile age estimation scheme we name LaGMO
This proposal characterized juvenile aging cues based on the 68 facial landmark points of the Active Appearance Model, where the shape and appearance features were presented as terms that described the age of the face
Summary
Age estimation enables the automatic tagging of a person’s age with a specific number or age bracket. Some researchers propose methods that utilize the facial landmark points, depicted, to represent aging features. The two prediction methods are combined hierarchically, first to classify age into the adult or juvenile group, and estimate age as a specific number within the target age-group [26,27,28]. When the age estimation task is within the same aging subspace such as juveniles (0 to 17 years), it becomes even more difficult to measure subtle changes in features that correspond to the age-group. Contributions: In this paper, we present the first-time use of the combination of facial landmark points and Term Frequency Inverse Gravity Moment (TF-IGM) to contribute a juvenile age estimation scheme we name LaGMO.
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