Proceedings ArticleDOI
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition
Luan Tran,Xi Yin,Xiaoming Liu +2 more
- pp 1283-1292
TLDR
Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.Abstract:
The large pose discrepancy between two face images is one of the key challenges in face recognition. Conventional approaches for pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform both tasks jointly to allow them to leverage each other. To this end, this paper proposes Disentangled Representation learning-Generative Adversarial Network (DR-GAN) with three distinct novelties. First, the encoder-decoder structure of the generator allows DR-GAN to learn a generative and discriminative representation, in addition to image synthesis. Second, this representation is explicitly disentangled from other face variations such as pose, through the pose code provided to the decoder and pose estimation in the discriminator. Third, DR-GAN can take one or multiple images as the input, and generate one unified representation along with an arbitrary number of synthetic images. Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.read more
Citations
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Proceedings ArticleDOI
Identity-Invariant Facial Landmark Frontalization For Facial Expression Analysis
TL;DR: A frontalization technique for 2D facial landmarks, designed to aid in the analysis of facial expressions, employs a new normalization strategy aiming to minimize identity variations, by displacing groups of facial landmarks to standardized locations.
Journal ArticleDOI
Information Maximization for Extreme Pose Face Recognition
Mohammad Saeed Ebrahimi Saadabadi,Sahar Rahimi Malakshan,Sobhan Soleymani,Moktari Mostofa,Nasser M. Nasrabadi +4 more
TL;DR: This paper uses a coupled-encoder network to project frontal/profile face images into a common latent embedding space and learns to enlarge the margin between the distribution of genuine and imposter faces, which results in high mutual information between different views of the same identity.
Proceedings ArticleDOI
Robust Video Facial Authentication With Unsupervised Mode Disentanglement
TL;DR: This work develops an unsupervised mode disentangling method for video facial authentication that shows significant face verification and identification performances on three publicly available datasets, KAIST-MPMI, UVA-NEMO, and YTF.
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Grab: Fast and Accurate Sensor Processing for Cashier-Free Shopping
TL;DR: This paper proposes Grab, a practical system that leverages existing infrastructure and devices to enable cashier-free shopping, which uses a keypoint-based pose tracker as a building block for identification and tracking, and develops robust feature-based face trackers, and algorithms for associating and tracking arm movements.
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Master Face Attacks on Face Recognition Systems.
TL;DR: Li et al. as discussed by the authors performed an extensive study on latent variable evolution (LVE), a method commonly used to generate master faces, and they run an LVE algorithm for various scenarios and with more than one database and face recognition system to study the properties of the master faces and to understand in which conditions strong master faces could be generated.
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