Integrating Vision and Language for First-Impression Personality Analysis

Gorbova, Jelena; Avots, Egils; Lüsi, Iiris; Fishel, Mark; Escalera, Sérgio; Anbarjafari, Gholamreza

doi:10.1109/mmul.2018.023121162

Cited by 13 publications

(7 citation statements)

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“…Finally, they combined the extracted features from audio, visual, and text at feature-level for the multimodal Big-five personality trait analysis and job interview recommendation. Gorbova et al (2017Gorbova et al ( , 2018 provided an automatic personality screening method on the basis of visual, audio, and text (lexical) cues from short video clips for predicting the Big-five personality traits. The extracted hand-crafted features contained acoustic LLD features (MFCCs, ZCR, speaking rate, etc.…”

Section: Bimodal Modalities Based Personality Trait Recognitionmentioning

confidence: 99%

Deep Personality Trait Recognition: A Survey

2022

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Automatic personality trait recognition has attracted increasing interest in psychology, neuropsychology, and computer science, etc. Motivated by the great success of deep learning methods in various tasks, a variety of deep neural networks have increasingly been employed to learn high-level feature representations for automatic personality trait recognition. This paper systematically presents a comprehensive survey on existing personality trait recognition methods from a computational perspective. Initially, we provide available personality trait data sets in the literature. Then, we review the principles and recent advances of typical deep learning techniques, including deep belief networks (DBNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Next, we describe the details of state-of-the-art personality trait recognition methods with specific focus on hand-crafted and deep learning-based feature extraction. These methods are analyzed and summarized in both single modality and multiple modalities, such as audio, visual, text, and physiological signals. Finally, we analyze the challenges and opportunities in this field and point out its future directions.

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Section: Bimodal Modalities Based Personality Trait Recognitionmentioning

confidence: 99%

Deep Personality Trait Recognition: A Survey

2022

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“…With the widespread use of AI and its applications in our everyday lives, accounting for fairness has gained significant importance in designing such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions, such as interviews [14], hiring [15] and personality analysis [16]. Thus, it is crucial to ensure that these decisions do not reflect discriminatory behaviour towards certain groups or populations.…”

Section: Related Workmentioning

confidence: 99%

Gender Neutralisation for Unbiased Speech Synthesising

Rizhinashvili

Sham²,

Anbarjafari

2022

Electronics

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Machine learning can encode and amplify negative biases or stereotypes already present in humans, resulting in high-profile cases. There can be multiple sources encoding the negative bias in these algorithms, like errors from human labelling, inaccurate representation of different population groups in training datasets, and chosen model structures and optimization methods. Our paper proposes a novel approach to speech processing that can resolve the gender bias problem by eliminating the gender parameter. Therefore, we devised a system that transforms the input sound (speech of a person) into a neutralized voice to the point where the gender of the speaker becomes indistinguishable by both humans and AI. Wav2Vec based network has been utilised to conduct speech gender recognition to validate the main claim of this research work, which is the neutralisation of gender from the speech. Such a system can be used as a batch pre-processing layer for training models, thus making associated gender bias irrelevant. Further, such a system can also find its application where speaker gender bias by humans is also prominent, as the listener will not be able to judge the gender from speech.

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“…In [ 26 , 27 ], audio, images (using OpenFace), and spoken text are extracted from the videos in the ChaLearn dataset. In both, there are 3 separate components or channels for processing and extracting characteristics, one for each modality taken, and at the end the results of each component are combined to obtain a personality prediction.…”

Section: State Of the Artmentioning

confidence: 99%

Deep-Learning-Based Adaptive Advertising with Augmented Reality

Moreno-Armendáriz

Calvo

Duchanoy³

et al. 2021

Sensors

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In this work we describe a system composed of deep neural networks that analyzes characteristics of customers based on their face (age, gender, and personality), as well as the ambient temperature, with the purpose of generating a personalized signal to potential buyers who pass in front of a beverage establishment; faces are automatically detected, displaying a recommendation using deep learning methods. In order to present suitable digital posters for each person, several technologies were used: Augmented reality, estimation of age, gender, and estimation of personality through the Big Five test applied to an image. The accuracy of each one of these deep neural networks is measured separately to ensure an appropriate precision over 80%. The system has been implemented into a portable solution, and is able to generate a recommendation to one or more people at the same time.

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Integrating Vision and Language for First-Impression Personality Analysis

Cited by 13 publications

References 13 publications

Deep Personality Trait Recognition: A Survey

Deep Personality Trait Recognition: A Survey

Gender Neutralisation for Unbiased Speech Synthesising

Deep-Learning-Based Adaptive Advertising with Augmented Reality

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