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

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.

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

“…[20] extracted Component extracted Bio-Inspired Feature (BIF) from facial landmarks using pyramid of convolution filters. [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].…”

Face Age Estimation Based on CSLBP and Lightweight Convolutional Neural Network

“…Their method employed the geometric proportions of the eyes, nose, and chin of the face as features for classification. Hammond et al [4] used BP neural network as a classifier on the basis of several features to divide age into four stages: children, youth, middle-aged, and old. However, as the age of the geometric model of the face tended to stabilize, the applicability of the model decreased.…”