Search Results for author:
Found 38 papers, 9 papers with code
EyePreserve: Identity-Preserving Iris Synthesis
Synthesis of same-identity biometric iris images, both for existing and non-existing identities while preserving the identity across a wide range of pupil sizes, is complex due to intricate iris muscle constriction mechanism, requiring a precise model of iris non-linear texture deformations to be embedded into the synthesis pipeline.
CRAFT: Contextual Re-Activation of Filters for face recognition Training
We show that CRAFT reduces fraction of inactive filters from 44% to 32% on average and discovers filter patterns not found by standard training.
LogicNet: A Logical Consistency Embedded Face Attribute Learning Network
We explore the potential reasons for this oversight and introduce two pressing challenges to the field: 1) How can we ensure that a model, when trained with data checked for logical consistency, yields predictions that are logically consistent?
Our Deep CNN Face Matchers Have Developed Achromatopsia
We show that such matchers achieve essentially the same accuracy on the grayscale or the color version of a set of test images.
Impact of Blur and Resolution on Demographic Disparities in 1-to-Many Facial Identification
This paper analyzes the accuracy of 1-to-many facial identification across demographic groups, and in the presence of blur and reduced resolution in the probe image as might occur in "surveillance camera quality" images.
What Should Be Balanced in a "Balanced" Face Recognition Dataset?
These datasets typically balance the number of identities and images across demographics.
Exploring Causes of Demographic Variations In Face Recognition Accuracy
In recent years, media reports have called out bias and racism in face recognition technology.
Logical Consistency and Greater Descriptive Power for Facial Hair Attribute Learning
We propose a logically consistent prediction loss, LCPLoss, to aid learning of logical consistency across attributes, and also a label compensation training strategy to eliminate the problem of no positive prediction across a set of related attributes.
Consistency and Accuracy of CelebA Attribute Values
Two annotators independently assigning attribute values shows that only 12 of 40 common attributes are assigned values with >= 95% consistency, and three (high cheekbones, pointed nose, oval face) have essentially random consistency.
The Gender Gap in Face Recognition Accuracy Is a Hairy Problem
We then demonstrate that when the data used to estimate recognition accuracy is balanced across gender for how hairstyles occlude the face, the initially observed gender gap in accuracy largely disappears.
Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem
This is the first work that we are aware of to explore how the level of brightness of the skin region in a pair of face images (rather than a single image) impacts face recognition accuracy, and to evaluate this as a systematic factor causing unequal accuracy across demographics.
Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphology
There is consensus in the research literature that face recognition accuracy is lower for females, who often have both a higher false match rate and a higher false non-match rate.
Digital and Physical-World Attacks on Remote Pulse Detection
Remote photoplethysmography (rPPG) is a technique for estimating blood volume changes from reflected light without the need for a contact sensor.
Deception Detection and Remote Physiological Monitoring: A Dataset and Baseline Experimental Results
Our application context is an interview scenario in which the interviewee attempts to deceive the interviewer on selected responses.
Analysis of Manual and Automated Skin Tone Assignments for Face Recognition Applications
News reports have suggested that darker skin tone causes an increase in face recognition errors.
Does Face Recognition Error Echo Gender Classification Error?
For impostor image pairs, our results show that pairs in which one image has a gender classification error have a better impostor distribution than pairs in which both images have correct gender classification, and so are less likely to generate a false match error.
Is Face Recognition Sexist? No, Gendered Hairstyles and Biology Are
There is consensus in the research literature that face recognition accuracy is lower for females, who often have both a higher false match rate and a higher false non-match rate.
Iris Presentation Attack Detection: Where Are We Now?
As the popularity of iris recognition systems increases, the importance of effective security measures against presentation attacks becomes paramount.
The Criminality From Face Illusion
A few recent publications have claimed success in analyzing an image of a person's face in order to predict the person's status as Criminal / Non-Criminal.
A Method for Curation of Web-Scraped Face Image Datasets
The numbers of subjects and images acquired in web-scraped datasets are usually very large, with number of images on the millions scale.
Robust Iris Presentation Attack Detection Fusing 2D and 3D Information
Diversity and unpredictability of artifacts potentially presented to an iris sensor calls for presentation attack detection methods that are agnostic to specificity of presentation attack instruments.
How Does Gender Balance In Training Data Affect Face Recognition Accuracy?
Deep learning methods have greatly increased the accuracy of face recognition, but an old problem still persists: accuracy is usually higher for men than women.
Analysis of Gender Inequality In Face Recognition Accuracy
We show that the female genuine distribution improves when only female images without facial cosmetics are used, but that the female impostor distribution also degrades at the same time.
Identity Document to Selfie Face Matching Across Adolescence
Matching live images (``selfies'') to images from ID documents is a problem that can arise in various applications.
Does Face Recognition Accuracy Get Better With Age? Deep Face Matchers Say No
In contrast, a pre deep learning matcher on the same dataset shows the traditional result of higher accuracy for older persons, although its overall accuracy is much lower than that of the deep learning matchers.
Iris Presentation Attack Detection Based on Photometric Stereo Features
2, 900 iris image pairs acquired from approx.
Binary Classification
Cross-Domain Iris Presentation Attack Detection
On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs
We propose a multi-scale GAN model to hallucinate realistic context (forehead, hair, neck, clothes) and background pixels automatically from a single input face mask.
Fast Face Image Synthesis with Minimal Training
Our method samples face components from a pool of multiple face images of real identities to generate the synthetic texture.
Open Source Presentation Attack Detection Baseline for Iris Recognition
This paper proposes the first, known to us, open source presentation attack detection (PAD) solution to distinguish between authentic iris images (possibly wearing clear contact lenses) and irises with textured contact lenses.
Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation-driven Verification
Results suggest that: (a) people improve their identity verification accuracy when asked to annotate matching and non-matching regions between the pair of images, (b) images depicting the same eye with large difference in pupil dilation were the most challenging to subjects, but benefited well from the annotation-driven classification, (c) humans performed better than iris recognition algorithms when verifying genuine pairs of post-mortem and disease-affected eyes (i. e., samples showing deformations that go beyond the distortions of a healthy iris due to pupil dilation), and (d) annotation does not improve accuracy of analyzing images from identical twins, which remain confusing for people.
Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition
One important point is that all applications of BSIF in iris recognition have used the original BSIF filters, which were trained on image patches extracted from natural images.
Beyond Pixels: Image Provenance Analysis Leveraging Metadata
Creative works, whether paintings or memes, follow unique journeys that result in their final form.
Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art
Different categories of presentation attack are described and placed in an application-relevant framework, and the state of the art in detecting each category of attack is summarized.
Image Provenance Analysis at Scale
Given a large corpus of images and a query image, an interesting further step is to retrieve the set of original images whose content is present in the query image, as well as the detailed sequences of transformations that yield the query image given the original images.
Demography-based Facial Retouching Detection using Subclass Supervised Sparse Autoencoder
However, previous work on this topic has not considered whether or how accuracy of retouching detection varies with the demography of face images.
SREFI: Synthesis of Realistic Example Face Images
In this paper, we propose a novel face synthesis approach that can generate an arbitrarily large number of synthetic images of both real and synthetic identities.
An Analysis of 1-to-First Matching in Iris Recognition
In order to improve the speed of large-scale identification, a modified "1-to-First" search has been used in some operational systems.
The ND-IRIS-0405 Iris Image Dataset
Image datasets acquired in 2004-2005 at Notre Dame with this LG 2200 have been used in the ICE 2005 and ICE 2006 iris biometric evaluations.
