Automated Screening of Job Candidate Based on Multimodal Video Processing

Gorbova, Jelena; Lüsi, Iiris; Litvin, Andre; Anbarjafari, Gholamreza

doi:10.1109/cvprw.2017.214

Cited by 44 publications

(17 citation statements)

References 13 publications

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“…Although this method provides high accuracy due to tracking motion through landmark points, yet it suffers from high computation time at the pre‐processing stage. In a very similar study [12], k‐means is applied to action units to select five keyframes. Also, in [13], Hajarolasvadi and Demirel applied the k‐means clustering algorithm to acoustic features and then generated the corresponding spectrogram of the selected key audio segments.…”

Section: Related Researchmentioning

confidence: 99%

Deep facial emotion recognition in video using eigenframes

Hajarolasvadi

Demirel

2020

IET image process

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Section: Related Researchmentioning

confidence: 99%

Deep facial emotion recognition in video using eigenframes

Hajarolasvadi

Demirel

2020

IET image process

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“…They use Hofstede's dimensions to create a simulated crowd from a cultural perspective. Gorbova and collaborators [21] present a system of automatic personality screening from video presentations in order to make a decision whether a person has to be invited to a job interview based on visual, audio and lexical cues. The work proposed by [13], presents a model to detect personality aspects based on the Big-five personality model using individuals behaviors automatically detected in video sequences.…”

Section: Related Workmentioning

confidence: 99%

How much do you perceive this? An analysis on perceptions of geometric features, personalities and emotions in virtual humans (Extended Version)

Araujo

Favaretto

Knob

et al. 2019

Preprint

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This work aims to evaluate people's perception regarding geometric features, personalities and emotions characteristics in virtual humans. For this, we use as a basis, a dataset containing the tracking files of pedestrians captured from spontaneous videos and visualized them as identical virtual humans. The goal is to focus on their behavior and not being distracted by other features. In addition to tracking files containing their positions, the dataset also contains pedestrian emotions and personalities detected using Computer Vision and Pattern Recognition techniques. We proceed with our analysis in order to answer the question if subjects can perceive geometric features as distances/speeds as well as emotions and personalities in video sequences when pedestrians are represented by virtual humans. Regarding the participants, an amount of 73 people volunteered for the experiment. The analysis was divided in two parts: i) evaluation on perception of geometric characteristics, such as density, angular variation, distances and speeds, and ii) evaluation on personality and emotion perceptions. Results indicate that, even without explaining to the participants the concepts of each personality or emotion and how they were calculated (considering geometric characteristics), in most of the cases, participants perceived the personality and emotion expressed by the virtual agents, in accordance with the available ground truth.

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“…For comparison, the table also reports system performance on an earlier published version of the system (Achmadnoer Sukma Wicaksana and Liem 2017) (which used a smaller feature set and did not yet optimize regression techniques). Furthermore, performance scores are reported for two other proposed solutions: the work in Gorbova et al (2017), employing similar features to ours, but with a multi-layered perceptron as statistical model; and the work in Kaya et al (2017), which obtained the highest accuracies of all participants in the quantitative Challenge. This latter work employed several state-of-the-art feature sets, some of which resulting from representations learned using deep neural networks, with considerably higher dimensionality than our features (thousands of feature dimensions).…”

Section: Quantitative Performancementioning

confidence: 99%

“…Comparison of quantitative performance (accuracy) between the system described as use case in this chapter (Achmadnoer Sukma Wicaksana 2017), an earlier version of the system presented at the ChaLearn workshop (Achmadnoer Sukma Wicaksana and Liem 2017), and two other proposed solutions for the ChaLearn Job Candidate Screening ChallengeCategoriesUse case system Earlier versionGorbova et al (2017) Kaya et al…”

mentioning

confidence: 99%

Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening

Liem

Langer

Demetriou

et al. 2018

The Springer Series on Challenges in Machine Learning

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In a rapidly digitizing world, machine learning algorithms are increasingly employed in scenarios that directly impact humans. This also is seen in job candidate screening. Data-driven candidate assessment is gaining interest, due to high scalability and more systematic assessment mechanisms. However, it will only be truly accepted and trusted if explainability and transparency can be guaranteed. The current chapter emerged from ongoing discussions between psychologists and computer scientists with machine learning interests, and discusses the job candidate screening problem from an interdisciplinary viewpoint. After introducing the general problem, we present a tutorial on common important methodological focus points in psychological and machine learning research. Following this, we both contrast and combine psychological and machine learning approaches, and present a use case example of a data-driven job candidate assessment system, intended to be explainable towards non-technical hiring specialists. In connection to this, we also give an overview of more traditional job candidate assessment approaches, and discuss considerations for optimizing the acceptability of technology-supported hiring solutions by relevant stakeholders. Finally, we present several recommendations on how interdisciplinary collaboration on the topic may be fostered.

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Automated Screening of Job Candidate Based on Multimodal Video Processing

Cited by 44 publications

References 13 publications

Deep facial emotion recognition in video using eigenframes

Deep facial emotion recognition in video using eigenframes

How much do you perceive this? An analysis on perceptions of geometric features, personalities and emotions in virtual humans (Extended Version)

Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening

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