## 2 Related Works
### Age Estimation  
Traditional age estimation methods based on handcrafted features encounter several challenges, such as the necessity to account for various influencing factors and their reliance on expert knowledge, the accuracy of which is often unverifiable [17].  
The rise of deep learning has led to the widespread adoption of convolutional neural networks (CNNs), which can automatically extract robust facial features and learn age-related information [9]. Since the introduction of deep learning, most age estimation methods have relied on CNNs. Age estimation methods are commonly categorized into four types [17]: (1) multi-class classification; (2) regression-based estimation; (3) deep label distribution learning (DLDL); (4) ranking-based learning. For example, a model called CNN2 ELM was proposed in [18], which combines CNNs with extreme learning machines (ELMs) for age learning, achieving promising results in the ChaLearn 2016 competition. Another notable approach is the Deep Expectation of Apparent Age (DEX) system proposed in [19], which utilizes softmax expectation refinement based on the VGG-16 network. The DEX system treats age estimation as a classification problem, estimating age by multiplying age labels with the class probability distribution derived from the softmax output of the final VGG-16 layer. Although the DEX system performed well on large-scale datasets like IMDB-WIKI and MORPH-2, it did not account for the ordinal relationships between ages, limiting its ability to model age continuity.  
Figure 1: In a comparison of various mainstream facial age estimation methods on the AFAD, CACD, and IMDB-WIKI datasets, our method achieved the best performance across multiple datasets. (Note: A higher cumulative score percentage indicates a lower age estimation error rate, and a smaller MAE indicates smaller age estimation errors.)  
Ranking-based methods improve performance by learning ordinal relationships among ages. For instance, OR-CNN [20] and Ranking-CNN [21] use multiple binary classifiers to determine whether the age of an input image exceeds a particular threshold, indirectly estimating age. These approaches better capture ordinal information but may not fully address the continuity of age. Deep Regression Forests (DRFs) [22] employ hierarchical decision trees to learn nonlinear features, achieving high accuracy in age estimation. However, regression-based methods often face performance degradation when data for certain age groups are sparse. Table 1 presents various age estimation methods under each category and their performance across different datasets.  
To mitigate these limitations, [PERSON] et al. [23] introduced a novel framework for ordinal regression tasks such as age estimation, ensuring classifier consistency and substantially enhancing prediction performance. Likewise, [PERSON] et al. [24] addressed label ambiguity in age estimation by optimizing feature and classifier learning. [PERSON] et al. [25] employed relative ranking (\(\rho\)-rank) with local and global \(\rho\)-regressors, coupled with iterative optimization, to achieve strong results in sequential regression tasks such as facial age estimation.