4,153 research outputs found
Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation
Cross-resolution face recognition has become a challenging problem for modern
deep face recognition systems. It aims at matching a low-resolution probe image
with high-resolution gallery images registered in a database. Existing methods
mainly leverage prior information from high-resolution images by either
reconstructing facial details with super-resolution techniques or learning a
unified feature space. To address this challenge, this paper proposes a new
approach that enforces the network to focus on the discriminative information
stored in the low-frequency components of a low-resolution image. A
cross-resolution knowledge distillation paradigm is first employed as the
learning framework. Then, an identity-preserving network, WaveResNet, and a
wavelet similarity loss are designed to capture low-frequency details and boost
performance. Finally, an image degradation model is conceived to simulate more
realistic low-resolution training data. Consequently, extensive experimental
results show that the proposed method consistently outperforms the baseline
model and other state-of-the-art methods across a variety of image resolutions
SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations
Due to the prevalence of scale variance in nature images, we propose to use
image scale as a self-supervised signal for Masked Image Modeling (MIM). Our
method involves selecting random patches from the input image and downsampling
them to a low-resolution format. Our framework utilizes the latest advances in
super-resolution (SR) to design the prediction head, which reconstructs the
input from low-resolution clues and other patches. After 400 epochs of
pre-training, our Super Resolution Masked Autoencoders (SRMAE) get an accuracy
of 82.1% on the ImageNet-1K task. Image scale signal also allows our SRMAE to
capture scale invariance representation. For the very low resolution (VLR)
recognition task, our model achieves the best performance, surpassing DeriveNet
by 1.3%. Our method also achieves an accuracy of 74.84% on the task of
recognizing low-resolution facial expressions, surpassing the current
state-of-the-art FMD by 9.48%
CCFace: Classification Consistency for Low-Resolution Face Recognition
In recent years, deep face recognition methods have demonstrated impressive
results on in-the-wild datasets. However, these methods have shown a
significant decline in performance when applied to real-world low-resolution
benchmarks like TinyFace or SCFace. To address this challenge, we propose a
novel classification consistency knowledge distillation approach that transfers
the learned classifier from a high-resolution model to a low-resolution
network. This approach helps in finding discriminative representations for
low-resolution instances. To further improve the performance, we designed a
knowledge distillation loss using the adaptive angular penalty inspired by the
success of the popular angular margin loss function. The adaptive penalty
reduces overfitting on low-resolution samples and alleviates the convergence
issue of the model integrated with data augmentation. Additionally, we utilize
an asymmetric cross-resolution learning approach based on the state-of-the-art
semi-supervised representation learning paradigm to improve discriminability on
low-resolution instances and prevent them from forming a cluster. Our proposed
method outperforms state-of-the-art approaches on low-resolution benchmarks,
with a three percent improvement on TinyFace while maintaining performance on
high-resolution benchmarks.Comment: 2023 IEEE International Joint Conference on Biometrics (IJCB
Image Resolution Susceptibility of Face Recognition Models
Face recognition approaches often rely on equal image resolution for
verification faces on two images. However, in practical applications, those
image resolutions are usually not in the same range due to different image
capture mechanisms or sources. In this work, we first analyze the impact of
image resolutions on the face verification performance with a state-of-the-art
face recognition model. For images, synthetically reduced to resolution, the verification performance drops from
increasingly down to almost . Especially, for cross-resolution image
pairs (one high- and one low-resolution image), the verification accuracy
decreases even further. We investigate this behavior more in-depth by looking
at the feature distances for every 2-image test pair. To tackle this problem,
we propose the following two methods: 1) Train a state-of-the-art
face-recognition model straightforward with low-resolution images
directly within each batch. \\ 2) Train a siamese-network structure and adding
a cosine distance feature loss between high- and low-resolution features. Both
methods show an improvement for cross-resolution scenarios and can increase the
accuracy at very low resolution to approximately . However, a
disadvantage is that a specific model needs to be trained for every
resolution-pair ...Comment: 19 pages, 15 figures, 2 table
Computational Mechanisms of Face Perception
The intertwined history of artificial intelligence and neuroscience has significantly impacted their development, with AI arising from and evolving alongside neuroscience. The remarkable performance of deep learning has inspired neuroscientists to investigate and utilize artificial neural networks as computational models to address biological issues. Studying the brain and its operational mechanisms can greatly enhance our understanding of neural networks, which has crucial implications for developing efficient AI algorithms. Many of the advanced perceptual and cognitive skills of biological systems are now possible to achieve through artificial intelligence systems, which is transforming our knowledge of brain function. Thus, the need for collaboration between the two disciplines demands emphasis. It\u27s both intriguing and challenging to study the brain using computer science approaches, and this dissertation centers on exploring computational mechanisms related to face perception.
Face recognition, being the most active artificial intelligence research area, offers a wealth of data resources as well as a mature algorithm framework. From the perspective of neuroscience, face recognition is an important indicator of social cognitive formation and neural development. The ability to recognize faces is one of the most important cognitive functions. We first discuss the problem of how the brain encodes different face identities. By using DNNs to extract features from complex natural face images and project them into the feature space constructed by dimension reduction, we reveal a new face code in the human medial temporal lobe (MTL), where neurons encode visually similar identities. On this basis, we discover a subset of DNN units that are selective for facial identity. These identity-selective units exhibit a general ability to discriminate novel faces. By establishing coding similarities with real primate neurons, our study provides an important approach to understanding primate facial coding. Lastly, we discuss the impact of face learning during the critical period. We identify a critical period during DNN training and systematically discuss the use of facial information by the neural network both inside and outside the critical period. We further provide a computational explanation for the critical period influencing face learning through learning rate changes. In addition, we show an alternative method to partially recover the model outside the critical period by knowledge refinement and attention shifting.
Our current research not only highlights the importance of training orientation and visual experience in shaping neural responses to face features and reveals potential mechanisms for face recognition but also provides a practical set of ideas to test hypotheses and reconcile previous findings in neuroscience using computer methods
Octuplet Loss: Make Face Recognition Robust to Image Resolution
Image resolution, or in general, image quality, plays an essential role in
the performance of today's face recognition systems. To address this problem,
we propose a novel combination of the popular triplet loss to improve
robustness against image resolution via fine-tuning of existing face
recognition models. With octuplet loss, we leverage the relationship between
high-resolution images and their synthetically down-sampled variants jointly
with their identity labels. Fine-tuning several state-of-the-art approaches
with our method proves that we can significantly boost performance for
cross-resolution (high-to-low resolution) face verification on various datasets
without meaningfully exacerbating the performance on high-to-high resolution
images. Our method applied on the FaceTransformer network achieves 95.12% face
verification accuracy on the challenging XQLFW dataset while reaching 99.73% on
the LFW database. Moreover, the low-to-low face verification accuracy benefits
from our method. We release our code to allow seamless integration of the
octuplet loss into existing frameworks
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