A Review of the Anatomical Basis of Facial Aging and Deep Learning-Based Recognition Methods

Facial aging is not merely a linear degeneration of the epidermis, but rather a complex, multilevel biomechanical cascade involving skeletal remodeling, dynamic redistribution of fat compartment volumes, and degradation of the skin matrix [1]. Modern anatomical studies have confirmed that subcutaneous fat is precisely divided into superficial and deep fat compartments with distinct boundaries and different aging trajectories, the atrophy of the deep fat compartments, combined with the displacement of the superficial fat compartments, leads to a significant reduction in facial three-dimensional volume [3]. At the same time, the functional decline of facial supporting ligaments is closely related to changes in the extracellular matrix's molecular structure, this reduction in tissue stiffness and tensile strength provides the pathophysiological basis for the transformation of dynamic wrinkles into static wrinkles [17]. In the field of computer vision, facial aging analysis primarily focuses on either inferring an individual’s physiological age from image sequences or enabling cross-age identity recognition, these technologies have broad application value in public security surveillance and digital forensics [7]. With the introduction of deep learning technologies, convolutional neural networks (CNNs) have become the mainstream analytical method in this field due to their exceptional feature extraction capabilities [8]. Addressing the significant variability in individual aging patterns, research has proposed models that incorporate attention mechanisms to achieve precise modeling of aging trajectories [11]. Generative adversarial networks (GANs), through age-conditional generation techniques, have successfully achieved age transformation while preserving identity features, effectively resolving the challenge of maintaining gender and racial consistency that traditional methods struggle with [14]. In age estimation tasks, ordinal regression and label distribution learning have further improved prediction accuracy by capturing the temporal order of age labels [13]. The core paradigm of cross-age face recognition is feature disentanglement, which aims to decompose facial representations into identity-inherent and age-related components, thereby minimizing the interference of age-related changes on identity recognition performance [12]. Despite significant progress, the field still faces severe challenges, such as insufficient data quality and diversity, as well as vast differences in individual aging patterns [7]. The “black-box” nature of deep neural networks results in a lack of sufficient theoretical explanation for the relationship between the learned aging representations and actual biological mechanisms, limiting their in-depth application in the biomedical field [15]. The scarcity of longitudinal paired data severely limits the models’ generalization ability, while the insufficient scale and diversity of mainstream datasets make overfitting a common issue [8]. Although the emerging line-scan confocal optical coherence tomography (OCT) technology can provide three-dimensional microscopic insights into the dermal fiber network, it still faces technical bottlenecks in aligning and standardizing multi-source data during clinical translation [16]. Future research must move beyond a mere race for performance and shift toward an in-depth exploration of the intrinsic structure and causal interpretability of aging characteristics. By integrating multi-source data, such as genomics, and establishing a new evaluation paradigm to assess the biological plausibility of models, thereby bridging the gap between deep network features and actual biological mechanisms of aging [37].