Learning Residual Images for Face Attribute Manipulation (1612.05363v2)

Abstract: Face attributes are interesting due to their detailed description of human faces. Unlike prior researches working on attribute prediction, we address an inverse and more challenging problem called face attribute manipulation which aims at modifying a face image according to a given attribute value. Instead of manipulating the whole image, we propose to learn the corresponding residual image defined as the difference between images before and after the manipulation. In this way, the manipulation can be operated efficiently with modest pixel modification. The framework of our approach is based on the Generative Adversarial Network. It consists of two image transformation networks and a discriminative network. The transformation networks are responsible for the attribute manipulation and its dual operation and the discriminative network is used to distinguish the generated images from real images. We also apply dual learning to allow transformation networks to learn from each other. Experiments show that residual images can be effectively learned and used for attribute manipulations. The generated images remain most of the details in attribute-irrelevant areas.

• The paper introduces a novel GAN framework that learns residual images for precise face attribute manipulation.
• It employs dual transformation networks with an inverse network to modify only attribute-specific areas while preserving essential identity details.
• Experimental validation on CelebA and LFW shows improved landmark detection accuracy and robustness against correlated visual features.

Summary of "Learning Residual Images for Face Attribute Manipulation"

The paper "Learning Residual Images for Face Attribute Manipulation" by Wei Shen and Rujie Liu tackles a more complex aspect of face image processing: modifying a face image to alter specific attributes, termed as face attribute manipulation. Unlike traditional methods that focus on attribute inference, this paper proposes manipulating face attributes by altering only the attribute-specific areas through the innovative concept of residual image learning.

Core Idea and Methodology

The authors propose a framework employing Generative Adversarial Networks (GANs), consisting of two image transformation networks and a single discriminative network. The core concept of their method revolves around residual image learning, where the manipulation is treated as a transformation to learn only the residual image—defined as the difference between the original and manipulated images. This allows for attribute-specific modifications while preserving the broader, attribute-irrelevant details of the image.

In this framework, one transformation network executes the primary attribute manipulation, while its counterpart performs the inverse. The discriminative network differentiates between real images and those generated by the transformation networks. Furthermore, dual learning is integrated, enabling the transformation networks to enhance their learning processes reciprocally.

Experiments and Results

The experimental validation focuses on two datasets: CelebA and Labeled Faces in the Wild (LFW). The proposed method demonstrates considerable effectiveness in maintaining image details outside the manipulated attribute-specific areas. Subjects included local attributes such as glasses and mouth openness, and global attributes, like age or gender. Notably, the method displays robustness against correlated visual features, a common challenge when handling biased datasets.

Remarkable improvements were observed in landmark detection accuracy post-glasses removal, an aspect evaluated during the experiments. Even though the landmark detection algorithm faced challenges when dealing with images having large poses, it underscored the practical utility of the manipulation method in preprocessing tasks.

Technical Merits

The integration of residual image learning focuses computational resources on essential modifications rather than redundantly processing whole images, aligning with the practical efficiencies demanded in large-scale image processing tasks. Moreover, this encourages the preservation of essential identity-related information, crucial for applications where identity consistency is necessary.

Additionally, the dual learning framework enhances overall model performance, capitalizing effectively on the GAN architecture. This setup indirectly creates a closed-loop learning environment between the transformation networks, promoting a more complete and comprehensive feature capture.

Implications and Future Directions

The implications of employing residual image learning in face attribute manipulation are significant. Practically, it offers efficient solutions for tasks that require subtle, targeted image modification without altering identity-defining features—useful in face recognition and authentication systems. The presentation of residual images as a sparse, focused learning target has the potential to inform solution methodologies dealing with domain-specific transformations in other areas of computer vision where attribute-irrelevant features dominate.

Future research might explore extending this framework beyond facial attributes into broader contexts, perhaps by integrating additional attribute-specific constraints or leveraging more advanced adversarial training regimes. Additionally, addressing limitations like variable pose handling may improve robustness across diverse datasets.

In conclusion, this paper contributes meaningfully to the domain of image processing by introducing a novel approach to face attribute manipulation that strategically focuses on essential image features while maintaining computational efficiency and result fidelity.