Age Progression/Regression by Conditional Adversarial Autoencoder (1702.08423v2)

Abstract: "If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5?" The answer is probably a "No." Most existing face aging works attempt to learn the transformation between age groups and thus would require the paired samples as well as the labeled query image. In this paper, we look at the problem from a generative modeling perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can directly produce the image with desired age attribute. We propose a conditional adversarial autoencoder (CAAE) that learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously. In CAAE, the face is first mapped to a latent vector through a convolutional encoder, and then the vector is projected to the face manifold conditional on age through a deconvolutional generator. The latent vector preserves personalized face features (i.e., personality) and the age condition controls progression vs. regression. Two adversarial networks are imposed on the encoder and generator, respectively, forcing to generate more photo-realistic faces. Experimental results demonstrate the appealing performance and flexibility of the proposed framework by comparing with the state-of-the-art and ground truth.

• The paper introduces a unified CAAE framework that achieves both age progression and regression without requiring paired samples while preserving identity.
• The methodology employs an encoder to capture personalized features and dual discriminators to enforce realistic outputs on a learned face manifold.
• Experimental results on datasets like Morph and CACD demonstrate that the framework produces high-fidelity images with smooth transitions and robust performance.

Age Progression/Regression by Conditional Adversarial Autoencoder

The paper "Age Progression/Regression by Conditional Adversarial Autoencoder" by Zhifei Zhang, Yang Song, and Hairong Qi presents a novel framework for facial age manipulation using Conditional Adversarial Autoencoder (CAAE). This work addresses the challenges of face aging and rejuvenation, focusing on generating photorealistic images that reflect both age progression and regression while preserving individual identity.

Proposed Approach

The authors tackle the problem from a generative modeling perspective by proposing a Conditional Adversarial Autoencoder (CAAE). The proposed method learns a face manifold and achieves smooth age progression and regression without requiring paired samples or labeled query images. The CAAE consists of three main components: an encoder, a generator, and two discriminators.

1. Encoder (E): Maps the input face image to a latent vector (z), capturing personalized facial features.
2. Generator (G): Produces a face image conditioned on both the latent vector (z) and an age label.
3. Discriminators:
   • $D_z$: Imposes a uniform distribution on the latent vector (z).
   • $D_{img}$: Ensures the generated face images are realistic and plausible for a given age.

The overall objective is to ensure that the generated faces lie on the learned manifold, closely resembling real faces at various ages.

Key Contributions

The paper's main contributions are summarized as follows:

• Unified Framework: The CAAE framework achieves both age progression and regression in a single model. This is a significant departure from traditional methods that typically require separate models and datasets for each direction.
• No Paired Samples Required: Unlike most existing methods, the proposed approach does not require paired samples of the same individual at different ages, making it more flexible and applicable to a broader range of real-world scenarios.
• Preserving Identity: By disentangling age and personality in the latent space, the model effectively preserves individual identity while modifying age-related features.
• Robustness: The approach is robust against variations in pose, expression, and occlusion, which are common challenges in facial image manipulation.

Experimental Results

The authors validate their approach on multiple datasets, including the Morph and CACD datasets, and additional images crawled from web searches. The results demonstrate that the CAAE framework can generate smooth and realistic age transitions. Qualitative comparisons with ground truth and state-of-the-art methods show that CAAE produces higher fidelity images with fewer artifacts.

A user paper was conducted to quantitatively evaluate the performance. The paper collected 3208 votes, with nearly half indicating that the generated images were similar to the ground truth. Further, a survey comparing CAAE's results with previous works showed a preference for CAAE in over 50% of the votes.

Implications and Future Directions

The proposed CAAE framework has several practical and theoretical implications:

• Practical Applications: The ability to generate realistic age-progressed and regressed images has applications in various fields such as missing person identification, entertainment, and age-invariant face verification.
• General Framework: The CAAE framework could be extended to other image manipulation tasks by conditioning on different attributes.
• Further Research: Future work could explore the use of this framework for cross-age recognition and age estimation. Additionally, improvements in network architecture and training strategies could further enhance image quality and computational efficiency.

Conclusion

The paper presents a well-rounded approach to the problem of age progression and regression, effectively handling the challenges of dataset requirements and identity preservation. By leveraging the strengths of adversarial autoencoders and introducing discriminative networks, the authors provide a flexible and robust solution that sets a new benchmark in the field of generative facial image manipulation. The potential applications and future extensions of this work hold significant promise for advancements in AI-driven facial recognition and age-related transformations.