## 2 Related Work  
FBP has received significant attention within computer vision as an emerging research area. Estimating the beauty level from a facial image could be treated as a classification [16; 17; 18], regression [19; 20], or ranking [21; 22] task. Two distinct categories of FBP exist. The first category employs a combination of hand-crafted features and conventional machine learning, whereas CNN and deep-learning techniques facilitate the second. Earlier research on FBP focused mostly on a set of hand-crafted features (geometric and texture) that led to the shallow machine-learning algorithms used to estimate facial aesthetics. [PERSON] et al. [23] considered a set of facial ratios as an objective of facial beauty criteria to be incorporated into ensemble-regression-based predictors to obtain the beauty score. However, geometric-feature-based techniques have limited performance due to the influence of facial-expression variation, and it demands a computational burden through landmark localizations. Psychological studies confirm that facial color, smoothness, and lightness are crucial for perceiving facial beauty [24; 25; 26]. [PERSON] et al. [27] implemented conventional image descriptors for texture feature extraction and combined them with putative facial ratios to predict the attractiveness score. In their experiments, the K-nearest neighbors (KNNs) achieved the highest performance for the combined features, with a Pearson correlation of 0.7836 and MSE of 0.0963, to outperform the other suggested models, such as linear regression, random forest, and artificial neural network (ANN), when evaluated on SCUT-FBP5500 dataset. Feature engineering was once considered a crucial part of computer vision applications before the emergence of deep learning. Recently, there has been an increasing demand for the automatic extraction of features from facial images. CNNs provide an end-to-end learning approach that can learn the mapping from the input to the desired output, eliminating the need for manual feature engineering. An ensemble CNN-based regression model was proposed in [28] to estimate the facial-beauty score automatically by utilizing a diverse learning environment and fuse the decision made by these ensembled networks to obtain a more reliable aesthetic score. However, sometimes the dominance of people with average beauty scores in the facial-beauty data space may pose a challenge when optimizing models using traditional loss functions, potentially resulting in extremely attractive and unattractive faces being treated as outliers. It is imperative to employ a hybrid loss function tailored based on the estimated tasks to predict multiple distinct but beauty-related tasks efficiently. Consequently, researchers have begun to explore models capable of handling outliers and multiple tasks simultaneously, resulting in improved performance compared to conventional regression approaches that rely heavily on L1 and L2 loss functions [29; 30]. For instance, [PERSON] et al. [31] utilized multiple loss functions for a CNN that simultaneously performed facial landmark localization and FBP tasks. Furthermore, a multitask CNN named HMTNet was introduced in [32]. HMTNet can predict the attractiveness score of a facial image, along with its race and gender. They devised a loss function called \"Smooth Huber Loss\". Similarly, [PERSON] et al. [33] employed the Huber loss function to assess various known pretrained CNNs on their recently introduced multi-ethnic dataset, as they anticipated a substantial number of outlier faces. However, the choice of the delta parameter in Huber loss can significantly impact the optimization process and ultimately the prediction accuracy, which may require additional hyperparameter tuning. [PERSON] et al. [34] developed an effective CNN based on the pretrained face-recognition model (CASIA-WebFace) and utilized a double activation layer with a new Softmax-MSE loss function to predict the facial beauty in Asian females. In the same context, [PERSON] et al.