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Found 18 papers, 0 papers with code
GroupMixNorm Layer for Learning Fair Models
Analysis on benchmark tabular and image datasets demonstrates the efficacy of the proposed method in achieving state-of-the-art performance.
Morality-based Assertion and Homophily on Social Media: A Cultural Comparison between English and Japanese Languages
Knowing that language is an inherent element of a culture, we used the social media platform Twitter to compare moral behaviors of Japanese tweets with English tweets.
Semi-supervised Learning for Marked Temporal Point Processes
Despite the research focus, limited attention has been given to the challenging problem of developing solutions in semi-supervised settings, where algorithms have access to a mix of labeled and unlabeled data.
Enhancing Fine-Grained Classification for Low Resolution Images
While fine-grained classification with high resolution images has received significant attention, limited attention has been given to low resolution images.
On the Robustness of Face Recognition Algorithms Against Attacks and Bias
Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications.
Dual Directed Capsule Network for Very Low Resolution Image Recognition
The proposed architecture utilizes a combination of capsule and convolutional layers for learning an effective VLR recognition model.
Deep Learning for Face Recognition: Pride or Prejudiced?
This research attempts to answer these questions and presents an in-depth analysis of `bias' in deep learning based face recognition systems.
A Comprehensive Overview of Biometric Fusion
A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is also presented.
Recognizing Disguised Faces in the Wild
While upcoming algorithms continue to achieve improved performance, a majority of the face recognition systems are susceptible to failure under disguise variations, one of the most challenging covariate of face recognition.
Supervised COSMOS Autoencoder: Learning Beyond the Euclidean Loss!
In this paper, we propose a novel Supervised COSMOS Autoencoder which utilizes a multi-objective loss function to learn representations that simultaneously encode the (i) "similarity" between the input and reconstructed vectors in terms of their direction, (ii) "distribution" of pixel values of the reconstruction with respect to the input sample, while also incorporating (iii) "discriminability" in the feature learning pipeline.
Learning A Shared Transform Model for Skull to Digital Face Image Matching
Human skull identification is an arduous task, traditionally requiring the expertise of forensic artists and anthropologists.
Class Representative Autoencoder for Low Resolution Multi-Spectral Gender Classification
Gender is one of the most common attributes used to describe an individual.
Residual Codean Autoencoder for Facial Attribute Analysis
Facial attributes can provide rich ancillary information which can be utilized for different applications such as targeted marketing, human computer interaction, and law enforcement.
Are you eligible? Predicting adulthood from face images via class specific mean autoencoder
Predicting if a person is an adult or a minor has several applications such as inspecting underage driving, preventing purchase of alcohol and tobacco by minors, and granting restricted access.
MagnifyMe: Aiding Cross Resolution Face Recognition via Identity Aware Synthesis
The proposed algorithm learns multi-level sparse representation for both high and low resolution gallery images, along with an identity aware dictionary and a transformation function between the two representations for face identification scenarios.
Face Sketch Matching via Coupled Deep Transform Learning
The performance of the proposed models is evaluated on a novel application of sketch-to-sketch matching, along with sketch-to-digital photo matching.
On Matching Skulls to Digital Face Images: A Preliminary Approach
Forensic application of automatically matching skull with face images is an important research area linking biometrics with practical applications in forensics.
Gender and Ethnicity Classification of Iris Images using Deep Class-Encoder
Soft biometric modalities have shown their utility in different applications including reducing the search space significantly.
