A Concatenational Graph Evolution Aging Model

Abstract

Modeling the long-term face aging process is of great importance for face recognition and animation, but there is a lack of sufficient long-term face aging sequences for model learning. To address this problem, we propose a CONcatenational GRaph Evolution (CONGRE) aging model, which adopts decomposition strategy in both spatial and temporal aspects to learn long-term aging patterns from partially dense aging databases. In spatial aspect, we build a graphical face representation, in which a human face is decomposed into mutually interrelated subregions under anatomical guidance. In temporal aspect, the long-term evolution of the above graphical representation is then modeled by connecting sequential short-term patterns following the Markov property of aging process under smoothness constraints between neighboring short-term patterns and consistency constraints among subregions. The proposed model also considers the diversity of face aging by proposing probabilistic concatenation strategy between short-term patterns and applying scholastic sampling in aging prediction. In experiments, the aging prediction results generated by the learned aging models are evaluated both subjectively and objectively to validate the proposed model.

Face Aging with Feature-Guide Conditional Generative Adversarial Network

“…Physical model-based methods: as seen in early face aging applications [4][5][6][7], intuitively adding or smoothing "age factors" to an image is a simple way to simulate the appearance of the face at a target age. The advantage of these methods is that they are easily applicable because they only require adding artificial elements to existing face images.…”

“…An ideal face aging algorithm should possess the following key characteristics: authenticity, identity preservation, and accuracy when generating images within the target age group. Previous research on facial aging has primarily focused on two categories of methods: physical model-based [4][5][6][7] and prototype-based [2,8,9]. Physical model-based methods rely on adding or removing age-related features, such as wrinkles, gray hair, or beards, that align with image generation rules.…”

Realistic Facial Age Transformation with 3D Uplifting

“…Additionally, the apparent age of a face can differ from the actual chronological age due to various factors such as genetics, lifestyle, environmental influences, and the presence of makeup. Early works in this field focus on building a physics‐based model [LTC02, SCS*12] or a prototype‐based method [KSSS14,TGMM12,TBP01] to simulate aging effects and facial details, such as wrinkles.…”

Gait-based age progression/regression: a baseline and performance evaluation by age group classification and cross-age gait identification

“…Currently, extensive studies on face-based age progression have been conducted, with approaches mainly classified into two groups: physical model-based and prototype-based. Physical model-based methods correlate biological facial aging patterns with human age using complex models, such as the statistical face model [18] and-or graph [40,41], concatenational graph evolution aging model [39], and craniofacial growth model [33]. By contrast, a limited number of works on facial age regression [9,18,21] have been based on physical models by simply removing textures from facial surfaces.…”

“…To reduce the appearance difference caused by the age gap between the gallery and probe, modeling the gait aging process is one of the possible solutions. More specifically, gait aging modeling includes a simulation with natural aging and reverse aging effects, that is, age progression (i.e., prediction of the future gait) and age regression (i.e., estimation of the previous gait), which is similar to the definition of aging and reverse aging effects considered in existing studies on face analysis [18,[38][39][40][41]43,47]. However, in real scenarios, such as surveillance, facial images may not work well because of low image resolution or even occlusion by a face mask.…”