Related papers: Deep User Identification Model with Multiple Biome…

Gender classification aims at recognizing a person's gender. Despite the high accuracy achieved by state-of-the-art methods for this task, there is still room for improvement in generalized and unrestricted datasets. In this paper, we…

In this paper, we propose a multi-task representation learning framework to jointly estimate the identity, gender and age of individuals from their hand images for the purpose of criminal investigations since the hand images are often the…

We present a cost-effective two-step authentication system that integrates face identification and speaker verification using only a camera and microphone available on common devices. The pipeline first performs face recognition to identify…

This work explores the utility of implicit behavioral cues, namely, Electroencephalogram (EEG) signals and eye movements for gender recognition (GR) and emotion recognition (ER) from psychophysical behavior. Specifically, the examined cues…

Throughout the past decade, many studies have classified human emotions using only a single sensing modality such as face video, electroencephalogram (EEG), electrocardiogram (ECG), galvanic skin response (GSR), etc. The results of these…

Objective: Multimodal hand gesture recognition (HGR) systems can achieve higher recognition accuracy compared to unimodal HGR systems. However, acquiring multimodal gesture recognition data typically requires users to wear additional…

Gait recognition aims to identify a person at a distance, serving as a promising solution for long-distance and less-cooperation pedestrian recognition. Recently, significant advancements in gait recognition have achieved inspiring success…

The development of various sensing technologies is improving measurements of stress and the well-being of individuals. Although progress has been made with single signal modalities like wearables and facial emotion recognition, integrating…

Multi-modal human action analysis is a critical and attractive research topic. However, the majority of the existing datasets only provide visual modalities (i.e., RGB, depth and skeleton). To make up this, we introduce a new, large-scale…

Multimodal learning involves integrating information from various modalities to enhance learning and comprehension. We compare three modality fusion strategies in person identification and verification by processing two modalities: voice…

Cross-domain biometrics has been emerging as a new necessity, which poses several additional challenges, including harsh illumination changes, noise, pose variation, among others. In this paper, we explore approaches to cross-domain face…

Recently, surface electromyogram (EMG) has been proposed as a novel biometric trait for addressing some key limitations of current biometrics, such as spoofing and liveness. The EMG signals possess a unique characteristic: they are…

Biometric-based verification is widely employed on the smartphones for various applications, including financial transactions. In this work, we present a new multimodal biometric dataset (face, voice, and periocular) acquired using a…

We present a quality-aware multimodal recognition framework that combines representations from multiple biometric traits with varying quality and number of samples to achieve increased recognition accuracy by extracting complimentary…

Human motion characteristics are used to monitor the progression of neurological diseases and mood disorders. Since perceptions of emotions are also interleaved with body posture and movements, emotion recognition from human gait can be…

Classification using multimodal data arises in many machine learning applications. It is crucial not only to model cross-modal relationship effectively but also to ensure robustness against loss of part of data or modalities. In this paper,…

The recent rapid advancements in both sensing and machine learning technologies have given rise to the universal collection and utilization of people's biometrics, such as fingerprints, voices, retina/facial scans, or gait/motion/gestures…

Mobile devices have been manufactured and enhanced at growing rates in the past decades. While this growth has significantly evolved the capability of these devices, their security has been falling behind. This contrast in development…

Human-machine interaction has been around for several decades now, with new applications emerging every day. One of the major goals that remain to be achieved is designing an interaction similar to how a human interacts with another human.…

In this paper, we propose a deep multimodal fusion network to fuse multiple modalities (face, iris, and fingerprint) for person identification. The proposed deep multimodal fusion algorithm consists of multiple streams of modality-specific…