51,368 research outputs found
Face Anti-Spoofing and Deep Learning Based Unsupervised Image Recognition Systems
One of the main problems of a supervised deep learning approach is that it requires large amounts of labeled training data, which are not always easily available. This PhD dissertation addresses the above-mentioned problem by using a novel unsupervised deep learning face verification system called UFace, that does not require labeled training data as it automatically, in an unsupervised way, generates training data from even a relatively small size of data. The method starts by selecting, in unsupervised way, k-most similar and k-most dissimilar images for a given face image. Moreover, this PhD dissertation proposes a new loss function to make it work with the proposed method. Specifically, the method computes loss function k times for both similar and dissimilar images for each input image in order to increase the discriminative power of feature vectors to learn the inter-class and intra-class face variability. The training is carried out based on the similar and dissimilar input face image vector rather than the same training input face image vector in order to extract face embeddings.
The UFace is evaluated on four benchmark face verification datasets: Labeled Faces in the Wild dataset (LFW), YouTube Faces dataset (YTF), Cross-age LFW (CALFW) and Celebrities in Frontal Profile in the Wild (CFP-FP) datasets. The results show that we gain an accuracy of 99.40\%, 96.04\%, 95.12\% and 97.89\% respectively. The achieved results, despite being unsupervised, is on par to a similar but fully supervised methods.
Another, related to face verification, area of research is on face anti-spoofing systems. State-of-the-art face anti-spoofing systems use either deep learning, or manually extracted image quality features. However, many of the existing image quality features used in face anti-spoofing systems are not well discriminating spoofed and genuine faces. Additionally, State-of-the-art face anti-spoofing systems that use deep learning approaches do not generalize well.
Thus, to address the above problem, this PhD dissertation proposes hybrid face anti-spoofing system that considers the best from image quality feature and deep learning approaches. This work selects and proposes a set of seven novel no-reference image quality features measurement, that discriminate well between spoofed and genuine faces, to complement the deep learning approach. It then, proposes two approaches: In the first approach, the scores from the image quality features are fused with the deep learning classifier scores in a weighted fashion. The combined scores are used to determine whether a given input face image is genuine or spoofed. In the second approach, the image quality features are concatenated with the deep learning features. Then, the concatenated features vector is fed to the classifier to improve the performance and generalization of anti-spoofing system.
Extensive evaluations are conducted to evaluate their performance on five benchmark face anti-spoofing datasets: Replay-Attack, CASIA-MFSD, MSU-MFSD, OULU-NPU and SiW. Experiments on these datasets show that it gives better results than several of the state-of-the-art anti-spoofing systems in many scenarios
Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domain
Face recognition technology has been deployed in various real-life
applications. The most sophisticated deep learning-based face recognition
systems rely on training millions of face images through complex deep neural
networks to achieve high accuracy. It is quite common for clients to upload
face images to the service provider in order to access the model inference.
However, the face image is a type of sensitive biometric attribute tied to the
identity information of each user. Directly exposing the raw face image to the
service provider poses a threat to the user's privacy. Current
privacy-preserving approaches to face recognition focus on either concealing
visual information on model input or protecting model output face embedding.
The noticeable drop in recognition accuracy is a pitfall for most methods. This
paper proposes a hybrid frequency-color fusion approach to reduce the input
dimensionality of face recognition in the frequency domain. Moreover, sparse
color information is also introduced to alleviate significant accuracy
degradation after adding differential privacy noise. Besides, an
identity-specific embedding mapping scheme is applied to protect original face
embedding by enlarging the distance among identities. Lastly, secure multiparty
computation is implemented for safely computing the embedding distance during
model inference. The proposed method performs well on multiple widely used
verification datasets. Moreover, it has around 2.6% to 4.2% higher accuracy
than the state-of-the-art in the 1:N verification scenario.Comment: This work is already accepted at the conference International
Conference on Computer Vision Theory and Applications (VISAPP) 2024 as a
regular pape
Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition
Two approaches are proposed for cross-pose face recognition, one is based on
the 3D reconstruction of facial components and the other is based on the deep
Convolutional Neural Network (CNN). Unlike most 3D approaches that consider
holistic faces, the proposed approach considers 3D facial components. It
segments a 2D gallery face into components, reconstructs the 3D surface for
each component, and recognizes a probe face by component features. The
segmentation is based on the landmarks located by a hierarchical algorithm that
combines the Faster R-CNN for face detection and the Reduced Tree Structured
Model for landmark localization. The core part of the CNN-based approach is a
revised VGG network. We study the performances with different settings on the
training set, including the synthesized data from 3D reconstruction, the
real-life data from an in-the-wild database, and both types of data combined.
We investigate the performances of the network when it is employed as a
classifier or designed as a feature extractor. The two recognition approaches
and the fast landmark localization are evaluated in extensive experiments, and
compared to stateof-the-art methods to demonstrate their efficacy.Comment: 14 pages, 12 figures, 4 table
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