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Reconstructing 3D faces with facial geometry from single images has allowed for major advances in animation, generative models, and virtual reality. However, this ability to represent faces with their 3D features is not as fully explored by the facial expression inference (FEI) community. This study therefore aims to investigate the impacts of integrating such 3D representations into the FEI task, specifically for facial expression classification and face-based valence-arousal (VA) estimation. To accomplish this, we first assess the performance of two 3D face representations (both based on the 3D morphable model, FLAME) for the FEI tasks. We further explore two fusion architectures, intermediate fusion and late fusion, for integrating the 3D face representations with existing 2D inference frameworks. To evaluate our proposed architecture, we extract the corresponding 3D representations and perform extensive tests on the AffectNet and RAF-DB datasets. Our experimental results demonstrate that our proposed method outperforms the state-of-the-art AffectNet VA estimation and RAF-DB classification tasks. Moreover, our method can act as a complement to other existing methods to boost performance in many emotion inference tasks. 

Sign language is a complex visual language, and automatic interpretations of sign language can facilitate communication involving deaf individuals. As one of the essential components of sign language, fingerspelling connects the natural spoken languages to the sign language and expands the scale of sign language vocabulary. In practice, it is challenging to analyze fingerspelling alphabets due to their signing speed and small motion range. The usage of synthetic data has the potential of further improving fingerspelling alphabets analysis at scale. In this paper, we evaluate how different video-based human representations perform in a framework for Alphabet Generation for American Sign Language (ASL). We tested three mainstream video-based human representations: twostream inflated 3D ConvNet, 3D landmarks of body joints, and rotation matrices of body joints. We also evaluated the effect of different skeleton graphs and selected body joints. The generation process of ASL fingerspelling used a transformerbased Conditional Variational Autoencoder. To train the model, we collected ASL alphabet signing videos from 17 signers with dynamic alphabet signing. The generated alphabets were evaluated using automatic metrics of quality such as FID, and we also considered supervised metrics by recognizing the generated entries using Spatio-Temporal Graph Convolutional Networks. Our experiments show that using the rotation matrices of the upper body joints and the signing hand give the best results for the generation of ASL alphabet signing. Going forward, our goal is to produce articulated fingerspelling words by combining individual alphabets learned in this work. 

Abstract Systemic inequity in biometrics systems based on racial and gender disparities has received a lot of attention recently. These disparities have been explored in existing biometrics systems such as facial biometrics (identifying individuals based on facial attributes). However, such ethical issues remain largely unexplored in voice biometric systems that are very popular and extensively used globally. Using a corpus of non-speech voice records featuring a diverse group of 300 speakers by race (75 each from White, Black, Asian, and Latinx subgroups) and gender (150 each from female and male subgroups), we explore and reveal that racial subgroup has a similar voice characteristic and gender subgroup has a significant different voice characteristic. Moreover, non-negligible racial and gender disparities exist in speaker identification accuracy by analyzing the performance of one commercial product and five research products. The average accuracy for Latinxs can be 12% lower than Whites (p < 0.05, 95% CI 1.58%, 14.15%) and can be significantly higher for female speakers than males (3.67% higher, p < 0.05, 95% CI 1.23%, 11.57%). We further discover that racial disparities primarily result from the neural network-based feature extraction within the voice biometric product and gender disparities primarily due to both voice inherent characteristic difference and neural network-based feature extraction. Finally, we point out strategies (e.g., feature extraction optimization) to incorporate fairness and inclusive consideration in biometrics technology. 

In this paper we present AMPNet, an acoustic abnormality detection model deployed at ACV Auctions to automatically identify engine faults of vehicles listed on the ACV Auctions platform. We investigate the problem of engine fault detection and discuss our approach of deep-learning based audio classification on a large-scale automobile dataset collected at ACV Auctions. Specifically, we discuss our data collection pipeline and its challenges, dataset preprocessing and training procedures, and deployment of our trained models into a production setting. We perform empirical evaluations of AMPNet and demonstrate that our framework is able to successfully capture various engine anomalies agnostic of vehicle type. Finally we demonstrate the effectiveness and impact of AMPNet in the real world, specifically showing a 20.85% reduction in vehicle arbitrations on ACV Auctions' live auction platform.