Temporal Non-Volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition

Understand

Modeling the long-term facial aging process is extremely challenging due to the presence of large and non-linear variations during the face development stages.

• In order to efficiently address the problem, this work first decomposes the aging process into multiple short-term stages.
• Then, a novel generative probabilistic model, named Temporal Non-Volume Preserving (TNVP) transformation, is presented to model the facial aging process at each stage.
• Unlike Generative Adversarial Networks (GANs), which requires an empirical balance threshold, and Restricted Boltzmann Machines (RBM), an intractable model, our proposed TNVP approach guarantees a tractable density function, exact inference and evaluation for embedding the feature transformations between faces in consecutive stages.