skip to main content


Title: Information Maximization for Extreme Pose Face Recognition
In this paper, we seek to draw connections between the frontal and profile face images in an abstract embedding space. We exploit this connection using a coupled-encoder network to project frontal/profile face images into a common latent embedding space. The proposed model forces the similarity of representations in the embedding space by maximizing the mutual information between two views of the face. The proposed coupled-encoder benefits from three contributions for matching faces with extreme pose disparities. First, we leverage our pose-aware contrastive learning to maximize the mutual information between frontal and profile representations of identities. Second, a memory buffer, which consists of latent representations accumulated over past iterations, is integrated into the model so it can refer to relatively much more instances than the minibatch size. Third, a novel pose-aware adversarial domain adaptation method forces the model to learn an asymmetric mapping from profile to frontal representation. In our framework, the coupled-encoder 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. The effectiveness of the proposed model is investigated through extensive experiments, evaluations, and ablation studies on four benchmark datasets, and comparison with the compelling state-of-the-art algorithms.  more » « less
Award ID(s):
1650474
NSF-PAR ID:
10401292
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
2022 IEEE International Joint Conference on Biometrics (IJCB), Abu Dhabi, United Arab Emirates
Page Range / eLocation ID:
1 to 10
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. In recent years, face recognition systems have achieved exceptional success due to promising advances in deep learning architectures. However, they still fail to achieve the expected accuracy when matching profile images against a gallery of frontal images. Current approaches either perform pose normalization (i.e., frontalization) or disentangle pose information for face recognition. We instead propose a new approach to utilize pose as auxiliary information via an attention mechanism. In this paper, we hypothesize that pose-attended information using an attention mechanism can guide contextual and distinctive feature extraction from profile faces, which further benefits better representation learning in an embedded domain. To achieve this, first, we design a unified coupled profile-to-frontal face recognition network. It learns the mapping from faces to a compact embedding subspace via a class-specific contrastive loss. Second, we develop a novel pose attention block (PAB) to specially guide the pose-agnostic feature extraction from profile faces. To be more specific, PAB is designed to explicitly help the network to focus on important features along both “channel” and “spatial” dimensions while learning discriminative yet pose-invariant features in an embedding subspace. To validate the effectiveness of our proposed method, we conduct experiments on both controlled and in the- wild benchmarks including Multi-PIE, CFP, and IJB-C, and show superiority over the state-of-the-art. 
    more » « less
  2. In this paper, we present a deep coupled learning framework to address the problem of matching polarimetric thermal face photos against a gallery of visible faces. Polarization state information of thermal faces provides the missing textural and geometrics details in the thermal face imagery which exist in visible spectrum. we propose a coupled deep neural network architecture which leverages relatively large visible and thermal datasets to overcome the problem of overfitting and eventually we train it by a polarimetric thermal face dataset which is the first of its kind. The proposed architecture is able to make full use of the polarimetric thermal information to train a deep model compared to the conventional shallow thermal-to-visible face recognition methods. Proposed coupled deep neural network also finds global discriminative features in a nonlinear embedding space to relate the polarimetric thermal faces to their corresponding visible faces. The results show the superiority of our method compared to the state-of-the-art models in cross thermal-to-visible face recognition algorithms. 
    more » « less
  3. Abstract—Materials Genomics initiative has the goal of rapidly synthesizing materials with a given set of desired properties using data science techniques. An important step in this direction is the ability to predict the outcomes of complex chemical reactions. Some graph-based feature learning algorithms have been proposed recently. However, the comprehensive relationship between atoms or structures is not learned properly and not explainable, and multiple graphs cannot be handled. In this paper, chemical reaction processes are formulated as translation processes. Both atoms and edges are mapped to vectors represent- ing the structural information. We employ the graph convolution layers to learn meaningful information of atom graphs, and further employ its variations, message passing networks (MPNN) and edge attention graph convolution network (EAGCN) to learn edge representations. Particularly, multi-view EAGCN groups and maps edges to a set of representations for the properties of the chemical bond between atoms from multiple views. Each bond is viewed from its atom type, bond type, distance and neighbor environment. The final node and edge representations are mapped to a sequence defined by the SMILES of the molecule and then fed to a decoder model with attention. To make full usage of multi-view information, we propose multi-view attention model to handle self correlation inside each atom or edge, and mutual correlation between edges and atoms, both of which are important in chemical reaction processes. We have evaluated our method on the standard benchmark datasets (that have been used by all the prior works), and the results show that edge embedding with multi-view attention achieves superior accuracy compared to existing techniques. 
    more » « less
  4. We propose a visually-grounded library of behaviors approach for learning to manipulate diverse objects across varying initial and goal configurations and camera placements. Our key innovation is to disentangle the standard image-to-action mapping into two separate modules that use different types of perceptual input:(1) a behavior selector which conditions on intrinsic and semantically-rich object appearance features to select the behaviors that can successfully perform the desired tasks on the object in hand, and (2) a library of behaviors each of which conditions on extrinsic and abstract object properties, such as object location and pose, to predict actions to execute over time. The selector uses a semantically-rich 3D object feature representation extracted from images in a differential end-to-end manner. This representation is trained to be view-invariant and affordance-aware using self-supervision, by predicting varying views and successful object manipulations. We test our framework on pushing and grasping diverse objects in simulation as well as transporting rigid, granular, and liquid food ingredients in a real robot setup. Our model outperforms image-to-action mappings that do not factorize static and dynamic object properties. We further ablate the contribution of the selector's input and show the benefits of the proposed view-predictive, affordance-aware 3D visual object representations. 
    more » « less
  5. Graph clustering is a fundamental problem in social network analysis, the goal of which is to group vertices of a graph into a series of densely knitted clusters with each cluster well separated from all the others. Classical graph clustering methods take advantage of the graph topology to model and quantify vertex proximity. With the proliferation of rich graph contents, such as user profiles in social networks, and gene annotations in protein interaction networks, it is essential to consider both the structure and content information of graphs for high-quality graph clustering. In this paper, we propose a graph embedding approach to clustering content-enriched graphs. The key idea is to embed each vertex of a graph into a continuous vector space where the localized structural and attributive information of vertices can be encoded in a unified, latent representation. Specifically, we quantify vertex-wise attribute proximity into edge weights, and employ truncated, attribute-aware random walks to learn the latent representations for vertices. We evaluate our attribute-aware graph embedding method in real-world attributed graphs, and the results demonstrate its effectiveness in comparison with state-of-the-art algorithms. 
    more » « less