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Found 20 papers, 1 papers with code
Deep Learning Models for Arrhythmia Classification Using Stacked Time-frequency Scalogram Images from ECG Signals
Due to the infeasibility of manual examination of large volumes of ECG data, this paper aims to propose an automated AI based system for ECG-based arrhythmia classification.
PatchBMI-Net: Lightweight Facial Patch-based Ensemble for BMI Prediction
However, the high computational requirement of these heavy-weight CNN models limits their deployment to resource-constrained mobile devices, thus deterring weight monitoring using smartphones.
Facial Forgery-based Deepfake Detection using Fine-Grained Features
As a countermeasure, a number of deepfake detection methods have been proposed.
MIS-AVoiDD: Modality Invariant and Specific Representation for Audio-Visual Deepfake Detection
In this paper, we tackle the problem at the representation level to aid the fusion of audio and visual streams for multimodal deepfake detection.
Is Facial Recognition Biased at Near-Infrared Spectrum As Well?
Interestingly, experimental results suggest equitable face recognition performance across gender and race at the NIR spectrum.
Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups
Published studies have suggested the bias of automated face-based gender classification algorithms across gender-race groups.
Ranked #1 on
Fairness
on MORPH
(using extra training data)
GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection
To this aim, we manually annotated existing popular deepfake datasets with gender labels and evaluated the performance differential of current deepfake detectors across gender.
An Examination of Bias of Facial Analysis based BMI Prediction Models
This is the reason for the least error rate of the facial analysis-based BMI prediction tool for Black Males and highest for White Females.
On Improving Cross-dataset Generalization of Deepfake Detectors
As a countermeasure, a number of deep fake detection methods have been proposed recently.
Compact CNN Models for On-device Ocular-based User Recognition in Mobile Devices
A number of studies have demonstrated the efficacy of deep learning convolutional neural network (CNN) models for ocular-based user recognition in mobile devices.
Harnessing Unlabeled Data to Improve Generalization of Biometric Gender and Age Classifiers
However, these deep learning models need large amount of labeled data for model training and optimum parameter estimation.
An Experimental Evaluation on Deepfake Detection using Deep Face Recognition
Experimental investigations on challenging Celeb-DF and FaceForensics++ deepfake datasets suggest the efficacy of deep face recognition in identifying deepfakes over two-class CNNs and the ocular modality.
Investigating Fairness of Ocular Biometrics Among Young, Middle-Aged, and Older Adults
A number of studies suggest bias of the face biometrics, i. e., face recognition and soft-biometric estimation methods, across gender, race, and age groups.
Towards On-Device Face Recognition in Body-worn Cameras
Face recognition technology in body-worn cameras is used for surveillance, situational awareness, and keeping the officer safe.
AILearn: An Adaptive Incremental Learning Model for Spoof Fingerprint Detection
AILearn is an adaptive incremental learning model which adapts to the features of the ``live'' and ``spoof'' fingerprint images and efficiently recognizes the new spoof fingerprints as well as the known spoof fingerprints when the new data is available.
Probing Fairness of Mobile Ocular Biometrics Methods Across Gender on VISOB 2.0 Dataset
To this aim, VISOB $2. 0$ dataset, along with its gender annotations, is used for the fairness analysis of ocular biometrics methods based on ResNet-50, MobileNet-V2 and lightCNN-29 models.
AI-based BMI Inference from Facial Images: An Application to Weight Monitoring
Self-diagnostic image-based methods for healthy weight monitoring is gaining increased interest following the alarming trend of obesity.
BWCFace: Open-set Face Recognition using Body-worn Camera
To this aim, the contribution of this work is two-fold: (1) collection of a dataset called BWCFace consisting of a total of 178K facial images of 132 subjects captured using the body-worn camera in in-door and daylight conditions, and (2) open-set evaluation of the latest deep-learning-based Convolutional Neural Network (CNN) architectures combined with five different loss functions for face identification, on the collected dataset.
Face Identification by SIFT-based Complete Graph Topology
This paper presents a new face identification system based on Graph Matching Technique on SIFT features extracted from face images.
