Face Aging With Conditional Generative Adversarial Networks (1702.01983v2)

Abstract: It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity. In this work, we propose the GAN-based method for automatic face aging. Contrary to previous works employing GANs for altering of facial attributes, we make a particular emphasize on preserving the original person's identity in the aged version of his/her face. To this end, we introduce a novel approach for "Identity-Preserving" optimization of GAN's latent vectors. The objective evaluation of the resulting aged and rejuvenated face images by the state-of-the-art face recognition and age estimation solutions demonstrate the high potential of the proposed method.

Face Aging with Conditional Generative Adversarial Networks

The paper "Face Aging with Conditional Generative Adversarial Networks" by Grigory Antipov, Moez Baccouche, and Jean-Luc Dugelay introduces a novel approach to synthesizing aged facial images while maintaining the identity of the original subject. This work leverages the capabilities of Generative Adversarial Networks (GANs) to achieve high-quality face aging transformations.

Methodology Overview

The authors propose the Age Conditional Generative Adversarial Network (Age-cGAN), a model designed to generate synthetic face images conditioned on specific age categories. The primary innovation lies in the "Identity-Preserving" optimization of GAN latent vectors, which ensures that the rendered aged images retain the identity of the input faces.

Key Contributions:

1. Age-cGAN Model: The design of Age-cGAN uniquely facilitates the generation of high-quality face images across distinct age categories, making significant advancements over existing GAN-based facial transformations.
2. Identity Preservation: A novel approach for optimizing latent vectors of GANs effectively maintains the identity of individuals in face aging tasks. The solution involves an optimization process minimizing the difference in identity features extracted by a face recognition network.

Experimental Results

The paper evaluates the performance of Age-cGAN using various methodologies:

• Synthetic Image Generation: The results demonstrate Age-cGAN's ability to disentangle latent identity features from age attributes, a vital aspect for maintaining identity while modifying age. The model's synthetic outputs exhibit realistic changes corresponding to target age categories.
• Objective Evaluation: Utilizing state-of-the-art face recognition networks like "OpenFace," the research quantifies identity retention, indicating that the proposed optimization approach achieves notable improvements in identity preservation—up to 82.9% recognition accuracy on reconstructed images.

Implications and Future Directions

The paper contributes significantly to the domain of image generation, particularly in applications where age-related transformations are crucial, such as cross-age face recognition and the development of age-progressive datasets. The findings suggest that this methodology can enhance robustness in face recognition systems used within fluctuating age contexts.

Future Research Prospects:

• Integration and Enhancement: The potential integration of pixel-level and identity-preserving optimizations suggests an avenue for further refinement and improved accuracy in retaining facial identity. Future work may explore the combination of these methods to bolster the efficacy of the latent vector optimization process.
• Broader Applications: Extending the use of identity-preserving techniques is contemplated for other facial modifications, including the addition of accessories or expressions, broadening the scope of practical applications in identity-conscience image synthesis.

The paper establishes a foundation for subsequent exploration and improvement in face aging methodologies, presenting a substantiated approach to overcoming the challenges of identity preservation within the generative modeling framework.