25 research outputs found
Grouped Knowledge Distillation for Deep Face Recognition
Compared with the feature-based distillation methods, logits distillation can
liberalize the requirements of consistent feature dimension between teacher and
student networks, while the performance is deemed inferior in face recognition.
One major challenge is that the light-weight student network has difficulty
fitting the target logits due to its low model capacity, which is attributed to
the significant number of identities in face recognition. Therefore, we seek to
probe the target logits to extract the primary knowledge related to face
identity, and discard the others, to make the distillation more achievable for
the student network. Specifically, there is a tail group with near-zero values
in the prediction, containing minor knowledge for distillation. To provide a
clear perspective of its impact, we first partition the logits into two groups,
i.e., Primary Group and Secondary Group, according to the cumulative
probability of the softened prediction. Then, we reorganize the Knowledge
Distillation (KD) loss of grouped logits into three parts, i.e., Primary-KD,
Secondary-KD, and Binary-KD. Primary-KD refers to distilling the primary
knowledge from the teacher, Secondary-KD aims to refine minor knowledge but
increases the difficulty of distillation, and Binary-KD ensures the consistency
of knowledge distribution between teacher and student. We experimentally found
that (1) Primary-KD and Binary-KD are indispensable for KD, and (2)
Secondary-KD is the culprit restricting KD at the bottleneck. Therefore, we
propose a Grouped Knowledge Distillation (GKD) that retains the Primary-KD and
Binary-KD but omits Secondary-KD in the ultimate KD loss calculation. Extensive
experimental results on popular face recognition benchmarks demonstrate the
superiority of proposed GKD over state-of-the-art methods.Comment: 9 pages, 2 figures, 7 tables, accepted by AAAI 202
ExFaceGAN: Exploring Identity Directions in GAN's Learned Latent Space for Synthetic Identity Generation
Deep generative models have recently presented impressive results in
generating realistic face images of random synthetic identities. To generate
multiple samples of a certain synthetic identity, several previous works
proposed to disentangle the latent space of GANs by incorporating additional
supervision or regularization, enabling the manipulation of certain attributes,
e.g. identity, hairstyle, pose, or expression. Most of these works require
designing special loss functions and training dedicated network architectures.
Others proposed to disentangle specific factors in unconditional pretrained
GANs latent spaces to control their output, which also requires supervision by
attribute classifiers. Moreover, these attributes are entangled in GAN's latent
space, making it difficult to manipulate them without affecting the identity
information. We propose in this work a framework, ExFaceGAN, to disentangle
identity information in state-of-the-art pretrained GANs latent spaces,
enabling the generation of multiple samples of any synthetic identity. The
variations in our generated images are not limited to specific attributes as
ExFaceGAN explicitly aims at disentangling identity information, while other
visual attributes are randomly drawn from a learned GAN latent space. As an
example of the practical benefit of our ExFaceGAN, we empirically prove that
data generated by ExFaceGAN can be successfully used to train face recognition
models.Comment: Accepted at IJCB 202
Online temporal detection of daily-living human activities in long untrimmed video streams
International audienceMany approaches were proposed to solve the problem of activity recognition in short clipped videos, which achieved impressive results with hand-crafted and deep features. However, it is not practical to have clipped videos in real life, where cameras provide continuous video streams in applications such as robotics, video surveillance, and smart-homes. Here comes the importance of activity detection to help recognizing and localizing each activity happening in long videos. Activity detection can be defined as the ability to localize starting and ending of each human activity happening in the video, in addition to recognizing each activity label. A more challenging category of human activities is the daily-living activities, such as eating, reading, cooking, etc, which have low inter-class variation and environment where actions are performed are similar. In this work we focus on solving the problem of detection of daily-living activities in untrimmed video streams. We introduce new online activity detection pipeline that utilizes single sliding window approach in a novel way; the classifier is trained with sub-parts of training activities, and an online frame-level early detection is done for sub-parts of long activities during detection. Finally, a greedy Markov model based post processing algorithm is applied to remove false detection and achieve better results. We test our approaches on two daily-living datasets, DAHLIA and GAADRD, outperforming state of the art results by more than 10%
Privacy-Preserving Face Recognition Using Random Frequency Components
The ubiquitous use of face recognition has sparked increasing privacy
concerns, as unauthorized access to sensitive face images could compromise the
information of individuals. This paper presents an in-depth study of the
privacy protection of face images' visual information and against recovery.
Drawing on the perceptual disparity between humans and models, we propose to
conceal visual information by pruning human-perceivable low-frequency
components. For impeding recovery, we first elucidate the seeming paradox
between reducing model-exploitable information and retaining high recognition
accuracy. Based on recent theoretical insights and our observation on model
attention, we propose a solution to the dilemma, by advocating for the training
and inference of recognition models on randomly selected frequency components.
We distill our findings into a novel privacy-preserving face recognition
method, PartialFace. Extensive experiments demonstrate that PartialFace
effectively balances privacy protection goals and recognition accuracy. Code is
available at: https://github.com/Tencent/TFace.Comment: ICCV 202