# Adaptive Age Estimation towards Imbalanced Datasets
###### Abstract  
Current age estimation datasets often have a skewed long-tail distribution with significant data imbalance, rather than an ideal uniform distribution for each category. The existing age estimation algorithms that rely on label distribution do not leverage data density information to address the issue of data imbalance. To solve the aforementioned problem, this paper proposes a novel method based on cost-sensitive learning, namely Data-Imbalance Adaptive Age Regression (DIAAR), for age estimation. DIAAR consists of two main modules: the adaptive soft label (ASL) module and the Data Density Smoothing (DDS) module. The ASL module embeds soft labels in the form of probability in the age regression. It assigns different degrees of soft labels adaptively to head and tail data based on their density, which helps balance the dataset. The DDS module further addresses data imbalance by revising data density through kernel smoothing and reweighting the loss function accordingly. Experiments on two benchmark datasets show that DIAAR can effectively deal with data imbalance and improve the accuracy of age estimation, achieving an average improvement of 8% over the baseline models. Moreover, this approach can be applied to various methods based on convolutional neural network models. age estimation; data imbalance; data density +
Footnote †: journal:  
Even though re-sampling can help balance the datasets, it is not a foolproof solution. On the one hand, over-sampling can result in the repeated learning of samples from the tail-classes, which can limit the diversity of the training dataset and potentially reduce the robustness of the resulting model. On the other hand, under-sampling can lead to a significant reduction in the number of samples in the head-classes, thereby limiting the model's ability to fully learn the variations within this category. In addition to re-sampling, another solution proposed at the algorithmic level is cost-sensitive learning. In these methods, weights are assigned to samples to match a given data distribution [14]. Weighting by inverse class frequency [11; 15] or a smoothed version of the inverse square root of class frequency [16; 17] are often adopted. Although cost-sensitive learning is simple and effective, applying it to age regression brings new challenges distinct from its classification counterpart. Firstly, the distance between continuous age labels holds meaningful information. That is, the adjacent images in the label space will be interrelated and influence each other. For example, consider a scenario where two ages have the same limited number of samples, but one belongs to the head-classes and the other belongs to the tail-classes. This situation can result in varying degrees of data imbalance between the two age groups. Moreover, unlike classification, certain target values may have no data at all, which motivates the need for target extrapolation and interpolation [18]. This paper proposes a novel method based on cost-sensitive learning for robust estimation in the presence of data imbalance, called Data-Imbalance Adaptive Age Regression (DIAAR). The proposed method consists of two key operations: adaptive soft label (ASL) and Data Density Smoothing (DDS). ASL utilizes soft labels in the form of probability during regression, and adaptively assigns different degrees of soft labels to the head and tail data in the long-tail distribution according to the data density, applying cost-sensitive learning to age estimation. DDS modifies data density through kernel smoothing and further alleviates data imbalance by reweighting the loss function with the revised data density, which can prevent over-sampling and under-sampling. The contributions of this work can be summarized as follows:  
* The adaptive soft label (ASL) module makes use of soft labels in the age regression, reducing the imbalance of the data. * The Data Density Smoothing (DDS) module smooths the distribution of data before reweighting the loss function.