Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors (1807.00966v2)

Abstract: In this paper, we present supervision-by-registration, an unsupervised approach to improve the precision of facial landmark detectors on both images and video. Our key observation is that the detections of the same landmark in adjacent frames should be coherent with registration, i.e., optical flow. Interestingly, the coherency of optical flow is a source of supervision that does not require manual labeling, and can be leveraged during detector training. For example, we can enforce in the training loss function that a detected landmark at frame${t-1}$ followed by optical flow tracking from frame${t-1}$ to frame$_t$ should coincide with the location of the detection at frame$_t$. Essentially, supervision-by-registration augments the training loss function with a registration loss, thus training the detector to have output that is not only close to the annotations in labeled images, but also consistent with registration on large amounts of unlabeled videos. End-to-end training with the registration loss is made possible by a differentiable Lucas-Kanade operation, which computes optical flow registration in the forward pass, and back-propagates gradients that encourage temporal coherency in the detector. The output of our method is a more precise image-based facial landmark detector, which can be applied to single images or video. With supervision-by-registration, we demonstrate (1) improvements in facial landmark detection on both images (300W, ALFW) and video (300VW, Youtube-Celebrities), and (2) significant reduction of jittering in video detections.

• The paper introduces Supervision-by-Registration, a novel unsupervised method that leverages geometric registration to enhance the precision of facial landmark detectors without requiring extensive annotated datasets.
• This approach improves landmark accuracy by capturing geometric transformations between images and correcting detected points based on their geometric consistency across varied inputs.
• The method provides a robust, cost-effective way to enhance existing systems and suggests new directions for integrating geometric transformations into broader AI models, particularly in resource-constrained settings.

Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors

The paper "Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors" introduces a method to enhance the precision of facial landmark detection systems without relying on traditional supervised learning techniques. The authors, Xuanyi Dong, Shoou-I Yu, Xinshuo Weng, Shih-En Wei, Yi Yang, and Yaser Sheikh, propose a novel approach integrating geometric registration with facial landmark detection.

Facial landmark detection is integral to numerous applications in computer vision, such as face recognition, expression analysis, and augmented reality. Despite significant progress in this area, prevailing techniques often depend on large annotated datasets, which are costly and time-consuming to generate. The paper addresses this limitation by presenting a complementary unsupervised method.

This paper outlines a process wherein the registration of facial images is employed to refine the localization quality of landmark detectors. The approach is predicated on capturing geometric transformations between facial images, thus facilitating the correction and alignment of detected landmarks across varied inputs. By concentrating on the geometric consistency of landmarks, this method advances precision without requiring extensive annotation.

The authors report strong numerical results demonstrating the efficacy of the proposed method. Experiments reveal notable improvements over traditional supervised methods, particularly in scenarios lacking substantial annotated datasets. The introduced technique provides a robust alternative that enhances detection accuracy while alleviating the burdens of dataset labeling.

The paper potentially influences both practical and theoretical facets of facial recognition research. Practically, it offers a cost-effective methodology for improving existing systems, which can be pivotal in industries prioritizing large-scale facial analytics without expansive resources. Theoretically, it opens discussions regarding the integration of geometric transformations in broader AI models, suggesting avenues for future exploration in improving model precision without supervision.

Future developments in AI may leverage this approach to refine unsupervised learning techniques extensively, expanding to other domains requiring precise object recognition and analysis. The implications for automated systems and real-world applications are considerable, suggesting a trajectory wherein precision is maintained even in resource-constrained settings.