Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition

Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109

Recent Trends in Deep Learning Based Personality Detection

Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection

Performance Analysis of State-of-the-Art Deep Learning Models in the Visual-Based Apparent Personality Detection

This paper analyses the performances of pre-trained deep learning models as feature extractors for apparent personality trait detection (APD) by utilising different statistical methods to find the best performing pre-trained model. Accuracy and computational cost were used to measure the model performance. Personality is measured using the Big Five Personality Schema. CNN-RNN networks were designed using VGG19, ResNet152, and VGGFace pre-trained models to measure the personality with scene data. The models were compared using the mean accuracy attained and the average time is taken for training and testing. Descriptive statistics, graphs, and inferential statistics were applied in model comparisons. Results convey that, ResNet152 model reported the highest mean accuracy in the test dataset (0.9077), followed by VGG19 with 0.9036; VGGFace recorded the lowest (0.8962). ResNet152 consumed more time than other architectures in model training and testing since the number of parameters is comparably higher than the other two architectures involved. Statistical test results prove no significant evidence to conclude that VGG19 and ResNet152 based CNN-RNN models performed differently. This leads to the conclusion that even with a comparably lower number of parameters VGG19 model performed well. The findings reveal that satisfactory accuracy is obtained with a limited number of frames extracted from videos since models achieved more than 90% accuracy

To Whom are You Talking? A Deep Learning Model to Endow Social Robots with Addressee Estimation Skills

Communicating shapes our social word. For a robot to be considered social and being consequently integrated in our social environment it is fundamental to understand some of the dynamics that rule human-human communication. In this work, we tackle the problem of Addressee Estimation, the ability to understand an utterance's addressee, by interpreting and exploiting non-verbal bodily cues from the speaker. We do so by implementing an hybrid deep learning model composed of convolutional layers and LSTM cells taking as input images portraying the face of the speaker and 2D vectors of the speaker's body posture. Our implementation choices were guided by the aim to develop a model that could be deployed on social robots and be efficient in ecological scenarios. We demonstrate that our model is able to solve the Addressee Estimation problem in terms of addressee localisation in space, from a robot ego-centric point of view.Comment: Accepted version of a paper published at 2023 International Joint Conference on Neural Networks (IJCNN). Please find the published version and info to cite the paper at https://doi.org/10.1109/IJCNN54540.2023.10191452 . 10 pages, 8 Figures, 3 Table

The Emotional Impact of Audio - Visual Stimuli

Induced affect is the emotional effect of an object on an individual. It can be quantified through two metrics: valence and arousal. Valance quantifies how positive or negative something is, while arousal quantifies the intensity from calm to exciting. These metrics enable researchers to study how people opine on various topics. Affective content analysis of visual media is a challenging problem due to differences in perceived reactions. Industry standard machine learning classifiers such as Support Vector Machines can be used to help determine user affect. The best affect-annotated video datasets are often analyzed by feeding large amounts of visual and audio features through machine-learning algorithms. The goal is to maximize accuracy, with the hope that each feature will bring useful information to the table. We depart from this approach to quantify how different modalities such as visual, audio, and text description information can aid in the understanding affect. To that end, we train independent models for visual, audio and text description. Each are convolutional neural networks paired with support vector machines to classify valence and arousal. We also train various ensemble models that combine multi-modal information with the hope that the information from independent modalities benefits each other. We find that our visual network alone achieves state-of-the-art valence classification accuracy and that our audio network, when paired with our visual, achieves competitive results on arousal classification. Each network is much stronger on one metric than the other. This may lead to more sophisticated multimodal approaches to accurately identifying affect in video data. This work also contributes to induced emotion classification by augmenting existing sizable media datasets and providing a robust framework for classifying the same

Towards Video Transformers for Automatic Human Analysis

[eng] With the aim of creating artificial systems capable of mirroring the nuanced understanding and interpretative powers inherent to human cognition, this thesis embarks on an exploration of the intersection between human analysis and Video Transformers. The objective is to harness the potential of Transformers, a promising architectural paradigm, to comprehend the intricacies of human interaction, thus paving the way for the development of empathetic and context-aware intelligent systems. In order to do so, we explore the whole Computer Vision pipeline, from data gathering, to deeply analyzing recent developments, through model design and experimentation. Central to this study is the creation of UDIVA, an expansive multi-modal, multi-view dataset capturing dyadic face-to-face human interactions. Comprising 147 participants across 188 sessions, UDIVA integrates audio-visual recordings, heart-rate measurements, personality assessments, socio- demographic metadata, and conversational transcripts, establishing itself as the largest dataset for dyadic human interaction analysis up to this date. This dataset provides a rich context for probing the capabilities of Transformers within complex environments. In order to validate its utility, as well as to elucidate Transformers' ability to assimilate diverse contextual cues, we focus on addressing the challenge of personality regression within interaction scenarios. We first adapt an existing Video Transformer to handle multiple contextual sources and conduct rigorous experimentation. We empirically observe a progressive enhancement in model performance as more context is added, reinforcing the potential of Transformers to decode intricate human dynamics. Building upon these findings, the Dyadformer emerges as a novel architecture, adept at long-range modeling of dyadic interactions. By jointly modeling both participants in the interaction, as well as embedding multi- modal integration into the model itself, the Dyadformer surpasses the baseline and other concurrent approaches, underscoring Transformers' aptitude in deciphering multifaceted, noisy, and challenging tasks such as the analysis of human personality in interaction. Nonetheless, these experiments unveil the ubiquitous challenges when training Transformers, particularly in managing overfitting due to their demand for extensive datasets. Consequently, we conclude this thesis with a comprehensive investigation into Video Transformers, analyzing topics ranging from architectural designs and training strategies, to input embedding and tokenization, traversing through multi-modality and specific applications. Across these, we highlight trends which optimally harness spatio-temporal representations that handle video redundancy and high dimensionality. A culminating performance comparison is conducted in the realm of video action classification, spotlighting strategies that exhibit superior efficacy, even compared to traditional CNN-based methods.[cat] Aquesta tesi busca crear sistemes artificials que reflecteixin les habilitats de comprensió i interpretació humanes a través de l'ús de Transformers per a vídeo. L'objectiu és utilitzar aquestes arquitectures per comprendre millor la interacció humana i desenvolupar sistemes intel·ligents i conscients de l'entorn. Això implica explorar àmplies àrees de la Visió per Computador, des de la recopilació de dades fins a l'anàlisi de l'estat de l'art i la prova experimental d'aquests models. Una part essencial d'aquest estudi és la creació d'UDIVA, un ampli conjunt de dades multimodal i multivista que enregistra interaccions humanes cara a cara. Amb 147 participants i 188 sessions, UDIVA inclou contingut audiovisual, freqüència cardíaca, perfils de personalitat, dades sociodemogràfiques i transcripcions de les converses. És el conjunt de dades més gran conegut per a l'anàlisi de la interacció humana diàdica i proporciona un context ric per a l'estudi de les capacitats dels Transformers en entorns complexos. Per tal de validar la seva utilitat i les habilitats dels Transformers, ens centrem en la regressió de la personalitat. Inicialment, adaptem un Transformer de vídeo per integrar diverses fonts de context. Mitjançant experiments exhaustius, observem millores progressives en els resultats amb la inclusió de més context, confirmant la capacitat dels Transformers. Motivats per aquests resultats, desenvolupem el Dyadformer, una arquitectura per interaccions diàdiques de llarga duració. Aquesta nova arquitectura considera simultàniament els dos participants en la interacció i incorpora la multimodalitat en un sol model. El Dyadformer supera la nostra proposta inicial i altres treballs similars, destacant la capacitat dels Transformers per abordar tasques complexes. No obstant això, aquestos experiments revelen reptes d'entrenament dels Transformers, com el sobreajustament, per la seva necessitat de grans conjunts de dades. La tesi conclou amb una anàlisi profunda dels Transformers per a vídeo, incloent dissenys arquitectònics, estratègies d'entrenament, preprocessament de vídeos, tokenització i multimodalitat. S'identifiquen tendències per gestionar la redundància i alta dimensionalitat de vídeos i es realitza una comparació de rendiment en la classificació d'accions a vídeo, destacant estratègies d'eficàcia superior als mètodes tradicionals basats en convolucions

Multiple attribute analysis for deep learning based apparent personality analysis

En este trabajo, abordamos el problema de la estimación de la personalidad aparente, es decir, la estimación de la impresión que personas tienen con sobre la personalidad de otra persona. El reconocimiento de rasgos de la personalidad aparente es un problema complejo con aplicaciones potenciales en campos como las interfaces afectivas, robótica social, marketing y publicidad adaptativa, sistemas de tutoría adaptativa, terapia psicológica y contratación laboral.In this work, we address the problem of apparent personality estimation, meaning the estimation of the impression other people have with respect to someone's personality. Apparent personality trait recognition is a complex problem with potential applications in fields such as affective interfaces, social robotics, adaptive marketing and advertising, adaptive tutoring systems, psychological therapy and job recruitment