On Face Segmentation, Face Swapping, and Face Perception

Abstract: We show that even when face images are unconstrained and arbitrarily paired, face swapping between them is actually quite simple. To this end, we make the following contributions. (a) Instead of tailoring systems for face segmentation, as others previously proposed, we show that a standard fully convolutional network (FCN) can achieve remarkably fast and accurate segmentations, provided that it is trained on a rich enough example set. For this purpose, we describe novel data collection and generation routines which provide challenging segmented face examples. (b) We use our segmentations to enable robust face swapping under unprecedented conditions. (c) Unlike previous work, our swapping is robust enough to allow for extensive quantitative tests. To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure the effect of intra- and inter-subject face swapping on recognition. We show that our intra-subject swapped faces remain as recognizable as their sources, testifying to the effectiveness of our method. In line with well known perceptual studies, we show that better face swapping produces less recognizable inter-subject results. This is the first time this effect was quantitatively demonstrated for machine vision systems.

• The paper presents a robust method for face swapping that uses a fast FCN for segmentation and quantitatively evaluates its impact on machine face recognition, finding inter-subject swaps reduce recognition accuracy.
• Using a trained FCN achieving high segmentation accuracy (approx. 83.7% IOU), the method shows that more effective swapping techniques decrease recognition accuracy on benchmarks like LFW for inter-subject cases.
• This research has significant implications for digital privacy and forensics, suggesting that advancements in face manipulation necessitate parallel developments in detection systems.

On Face Segmentation, Face Swapping, and Face Perception

The paper "On Face Segmentation, Face Swapping, and Face Perception" presents a comprehensive study of facial manipulation techniques with particular focus on face segmentation and swapping. Without hyperbolic claims, it methodically departs from existing literature by evaluating these techniques quantitatively, while also considering implications on face recognition and security applications.

The authors identify the utility of a Fully Convolutional Network (FCN) in achieving fast and accurate segmentations when trained with a comprehensive example set. Face segmentation, a precursor to swapping, benefits here from semi-supervised labeling utilizing motion cues from videos, augmented through 3D face models and occlusion simulations. The proposed FCN achieves an intersection over union (IOU) score of approximately 83.7% on the COFW dataset, reflecting near state-of-the-art performance while operating at substantial speeds.

The core contribution of the paper is its robust approach to face swapping, even when dealing with arbitrarily paired, unconstrained images differing in expressions, viewpoints, and other attributes. Swapping is deemed effective if inter-subject swaps result in indistinguishable source faces, rendering them less recognizable by machines—a notion first observed in human vision systems over two decades ago by Sinha and Poggio. For intra-subject swaps, the output images maintain the identity of the subject, endorsing the method's artifact avoidance capability.

A methodical evaluation was carried out using the Labeled Faces in the Wild (LFW) benchmark. Inter-subject swapping showed recognition accuracy decrement as better segmentation and 3D shape estimation techniques were employed, substantiating the claim that robust face swapping alters facial identity more effectively. Conversely, intra-subject swapping demonstrated negligible impact on face recognition accuracy, indicating the method's ability to preserve identity across varying expressions and conditions.

The paper, while maintaining focus on face swapping's quantitative assessment, insinuates broader implications for privacy protection and digital forensics, intimating that these techniques could be strategically employed to obfuscate identities in digital imagery without loss of realism.

In exploring future directions, the implications of this work blur the lines between artificial intelligence, security, and privacy domains. Increasingly sophisticated face swapping technologies, as they grow more imperceptible to existing biometric systems, will undoubtedly stir advancements in systems designed to detect such manipulations. The findings point toward a dual path of advancing both generation and detection capabilities, a reflection of the balance modern AI research must strike as it evolves.

Lastly, the accessibility of the codes and models online signals a commitment to fostering transparency and collaboration within the community. This, in turn, sets a precedent for improving reproducibility standards, which is imperative as research in computer vision and machine learning complicates.

In conclusion, the research dispassionately provides a valuable proof-of-concept for high fidelity face swapping, bolstering future explorations within AI's perpetual balancing act of synthesis and discrimination.