@article{khare2024emotion,
  title={Emotion recognition and artificial intelligence: A systematic review (2014--2023) and research recommendations},
  author={Khare, Smith K and Blanes-Vidal, Victoria and Nadimi, Esmaeil S and Acharya, U Rajendra},
  journal={Information Fusion},
  volume={102},
  pages={102019},
  year={2024},
  publisher={Elsevier}
}

@article{jekauc2024recognizing,
  title={Recognizing affective states from the expressive behavior of tennis players using convolutional neural networks},
  author={Jekauc, Darko and Burkart, Diana and Fritsch, Julian and Hesenius, Marc and Meyer, Ole and Sarfraz, Saquib and Stiefelhagen, Rainer},
  journal={Knowledge-Based Systems},
  volume={295},
  pages={111856},
  year={2024},
  publisher={Elsevier}
}

@article{coleman2024lessons,
  title={Lessons Learned From Conducting Virtual Multiple Mini Interviews During the COVID-19 Pandemic},
  author={Coleman, Veronica and Tran, Bau and Wang, Jijia and Sengupta, Samarpita and Perez, Isela Edna and Howell-Stampley, Temple and Jones, P Eugene},
  journal={The Journal of Physician Assistant Education},
  volume={35},
  number={3},
  pages={287--292},
  year={2024},
  publisher={LWW}
}

@article{dando2023interviewing,
  title={Interviewing in virtual environments: Towards understanding the impact of rapport-building behaviours and retrieval context on eyewitness memory},
  author={Dando, Coral and Taylor, Donna A and Caso, Alessandra and Nahouli, Zacharia and Adam, Charlotte},
  journal={Memory \& cognition},
  volume={51},
  number={2},
  pages={404--421},
  year={2023},
  publisher={Springer}
}


@article{albert2019ai,
  title={AI in talent acquisition: a review of AI-applications used in recruitment and selection},
  author={Albert, Edward Tristram},
  journal={Strategic HR Review},
  volume={18},
  number={5},
  pages={215--221},
  year={2019},
  publisher={Emerald Publishing Limited}
}

@inproceedings{sumathi2024ai,
  title={AI-Driven Interviewer: Enhancing Interview Experience Through Conversational AI},
  author={Sumathi, S and Kevin Harris, D and Jeyanth, AK},
  booktitle={International Conference on Computational Intelligence in Data Science},
  pages={180--196},
  year={2024},
  organization={Springer}
}

@misc{mckinseyState2023,
	author = {},
	title = {{T}he state of {A}{I} in 2023: {G}enerative {A}{I}’s breakout year --- mckinsey.com},
	howpublished = {\url{https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year}},
	year = {},
	note = {[Accessed 02-10-2024]},
}

@article{yadav2024adopting,
  title={Adopting artificial intelligence (AI) for employee recruitment: the influence of contextual factors},
  author={Yadav, Swati and Kapoor, Shikha},
  journal={International Journal of System Assurance Engineering and Management},
  volume={15},
  number={5},
  pages={1828--1840},
  year={2024},
  publisher={Springer}
}

@article{vinciarelli2011bridging,
  title={Bridging the gap between social animal and unsocial machine: A survey of social signal processing},
  author={Vinciarelli, Alessandro and Pantic, Maja and Heylen, Dirk and Pelachaud, Catherine and Poggi, Isabella and D'Errico, Francesca and Schroeder, Marc},
  journal={IEEE Transactions on Affective Computing},
  volume={3},
  number={1},
  pages={69--87},
  year={2011},
  publisher={IEEE}
}
@misc{ekman1997basic,
  author = {Paul Ekman},
  title = {Are There Basic Emotions?},
  year = {1997},
  url = {https://www.paulekman.com/wp-content/uploads/2013/07/Are-There-Basic-Emotions1.pdf}
}

@article{tromp2014rule,
  author = {Erik Tromp and Mykola Pechenizkiy},
  title = {Rule-based emotion detection on social media: Putting tweets on Plutchik’s wheel},
  month = {December},
  year = {2014},
}

@book{ortonycognitive,
  author = {Andrew Ortony and Gerald Clore and Allan Collins},
  title = {The Cognitive Structure of Emotion},
  volume = {18},
  month = {January},
  year = {1988},
}

@article{cowen2018mapping,
  author = {Alan Cowen and Hillary Elfenbein and Petri Laukka and Dacher Keltner},
  title = {Mapping 24 emotions conveyed by brief human vocalization},
  journal = {American Psychologist},
  month = {December},
  year = {2018},
}

@article{demszky2020goemotions,
  author = {Dorottya Demszky and Dana Movshovitz-Attias and Jeongwoo Ko and Alan Cowen and Gaurav Nemade and Sujith Ravi},
  title = {GoEmotions: A dataset of fine-grained emotions},
  year = {2020},
}

@inproceedings{strapparava2007semeval,
  author = {Carlo Strapparava and Rada Mihalcea},
  title = {SemEval-2007 task 14: Affective text},
  booktitle = {Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)},
  pages = {70--74},
  month = {June},
  year = {2007},
  address = {Prague, Czech Republic},
  organization = {Association for Computational Linguistics},
}

@misc{figureeight2016sentiment,
  title = {Sentiment Analysis and Emotion in Text},
  year = {2016},
  url = {https://www.figure-eight.com/data/sentiment-analysis-emotion-text/},
}

@inproceedings{alm2005emotions,
  author = {Cecilia Ovesdotter Alm and Dan Roth and Richard Sproat},
  title = {Emotions from text: Machine learning for text-based emotion prediction},
  booktitle = {Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing},
  pages = {579--586},
  month = {October},
  year = {2005},
  address = {Vancouver, British Columbia, Canada},
  organization = {Association for Computational Linguistics},
}

@inproceedings{ohman2018creating,
  author = {Emily O¨hman and Kaisla Kajava and Jo¨rg Tiedemann and Timo Honkela},
  title = {Creating a dataset for multilingual fine-grained emotion-detection using gamification-based annotation},
  booktitle = {Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  pages = {24--30},
  month = {October},
  year = {2018},
  address = {Brussels, Belgium},
  organization = {Association for Computational Linguistics},
}

@inproceedings{ghazi2015detecting,
  author = {Diman Ghazi and Diana Inkpen and Stan Szpakowicz},
  title = {Detecting emotion stimuli in emotion-bearing sentences},
  booktitle = {International Conference on Intelligent Text Processing and Computational Linguistics},
  pages = {152--165},
  year = {2015},
  publisher = {Springer},
}

@misc{goodfellow2013challengesrepresentationlearningreport,
      title={Challenges in Representation Learning: A report on three machine learning contests}, 
      author={Ian J. Goodfellow and Dumitru Erhan and Pierre Luc Carrier and Aaron Courville and Mehdi Mirza and Ben Hamner and Will Cukierski and Yichuan Tang and David Thaler and Dong-Hyun Lee and Yingbo Zhou and Chetan Ramaiah and Fangxiang Feng and Ruifan Li and Xiaojie Wang and Dimitris Athanasakis and John Shawe-Taylor and Maxim Milakov and John Park and Radu Ionescu and Marius Popescu and Cristian Grozea and James Bergstra and Jingjing Xie and Lukasz Romaszko and Bing Xu and Zhang Chuang and Yoshua Bengio},
      year={2013},
      eprint={1307.0414},
      archivePrefix={arXiv},
      primaryClass={stat.ML},
      url={https://arxiv.org/abs/1307.0414}, 
}

@article{Georgescu_2019,
   title={Local Learning With Deep and Handcrafted Features for Facial Expression Recognition},
   volume={7},
   ISSN={2169-3536},
   url={http://dx.doi.org/10.1109/ACCESS.2019.2917266},
   DOI={10.1109/access.2019.2917266},
   journal={IEEE Access},
   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
   author={Georgescu, Mariana-Iuliana and Ionescu, Radu Tudor and Popescu, Marius},
   year={2019},
   pages={64827–64836} 
}

@INPROCEEDINGS{9411919,
  author={Pham, Luan and Vu, The Huynh and Tran, Tuan Anh},
  booktitle={2020 25th International Conference on Pattern Recognition (ICPR)}, 
  title={Facial Expression Recognition Using Residual Masking Network}, 
  year={2021},
  volume={},
  number={},
  pages={4513-4519},
  keywords={Human computer interaction;Face recognition;Buildings;Deep architecture;Task analysis;Testing;Residual neural networks;Facial Expression Recognition;Masking Idea;Residual Masking Network},
  doi={10.1109/ICPR48806.2021.9411919}
}

@INPROCEEDINGS{9154121,
  author={Jaiswal, Akriti and Krishnama Raju, A. and Deb, Suman},
  booktitle={2020 International Conference for Emerging Technology (INCET)}, 
  title={Facial Emotion Detection Using Deep Learning}, 
  year={2020},
  volume={},
  number={},
  pages={1-5},
  keywords={Computational modeling;Machine learning;Feature extraction;Training;Neural networks;Data models;Face;Artificially intelligence (AI);Facial emotion recognition (FER);Convolutional neural networks (CNN);Rectified linear units (ReLu);Deep learning (DL)},
  doi={10.1109/INCET49848.2020.9154121}
}

@INPROCEEDINGS{9647334,
  author={Luo, Jinyu and Xie, Zhuocheng and Zhu, Feiyao and Zhu, Xiaohu},
  booktitle={2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC)}, 
  title={Facial Expression Recognition using Machine Learning models in FER2013}, 
  year={2021},
  volume={},
  number={},
  pages={231-235},
  keywords={Support vector machines;Machine learning algorithms;Face recognition;Conferences;Computational modeling;Machine learning;Data models;Facial Expression Recognition;Machine Learning;Data Augmentation;Feature Extraction},
  doi={10.1109/ICFTIC54370.2021.9647334}
}

@INPROCEEDINGS{10071193,
  author={Mao, Yuebin},
  booktitle={2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)}, 
  title={Optimization of Facial Expression Recgonition on ResNet-18 using Focal Loss and CosFace Loss}, 
  year={2022},
  volume={},
  number={},
  pages={161-163},
  keywords={Training;Image recognition;Face recognition;Lighting;Data models;Entropy;Ensemble learning;Facial expression recognition;focal loss;CosFace;loss function;FER2013},
  doi={10.1109/ISAIEE57420.2022.00041}
}

@article{10.1145/3587038,
author = {Wu, Yirui and Zhang, Lilai and Gu, Zonghua and Lu, Hu and Wan, Shaohua},
title = {Edge-AI-Driven Framework with Efficient Mobile Network Design for Facial Expression Recognition},
year = {2023},
issue_date = {May 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {22},
number = {3},
issn = {1539-9087},
url = {https://doi.org/10.1145/3587038},
doi = {10.1145/3587038},
abstract = {Facial Expression Recognition (FER) in the wild poses significant challenges due to realistic occlusions, illumination, scale, and head pose variations of the facial images. In this article, we propose an Edge-AI-driven framework for FER. On the algorithms aspect, we propose two attention modules, Arbitrary-oriented Spatial Pooling (ASP) and Scalable Frequency Pooling (SFP), for effective feature extraction to improve classification accuracy. On the systems aspect, we propose an edge-cloud joint inference architecture for FER to achieve low-latency inference, consisting of a lightweight backbone network running on the edge device, and two optional attention modules partially offloaded to the cloud. Performance evaluation demonstrates that our approach achieves a good balance between classification accuracy and inference latency.},
journal = {ACM Trans. Embed. Comput. Syst.},
month = {apr},
articleno = {57},
numpages = {17},
keywords = {cloud offloading, edge computing, Facial Expression Recognition, Deep learning}
}

@InProceedings{10.1007/978-981-99-7025-4_26,
author="Hassan, Saif
and Ullah, Mohib
and Imran, Ali Shariq
and Cheikh, Faouzi Alaya",
editor="Liu, Fenrong
and Sadanandan, Arun Anand
and Pham, Duc Nghia
and Mursanto, Petrus
and Lukose, Dickson",
title="Attention-Guided Self-supervised Framework for Facial Emotion Recognition",
booktitle="PRICAI 2023: Trends in Artificial Intelligence",
year="2024",
publisher="Springer Nature Singapore",
address="Singapore",
pages="294--306",
abstract="Facial expression recognition is pivotal in computer vision and finds applications across various domains. In this paper, we proposed a self-supervised learning approach for precise facial expression recognition. Our approach leverages recent advancements in diffusion models, specifically the Classification and Regression Diffusion (CARD) model. To enhance the discriminative capability of our model, we integrate the Convolutional Block Attention Module (CBAM), an effective attention mechanism, to extract pertinent and discriminative feature maps. Furthermore, we capitalize on unlabelled data by using the simple contrastive learning framework of self-supervised learning (SSL) to extract meaningful features. To evaluate the performance, we conduct extensive experiments on the FER2013 dataset, comparing our results with existing benchmarks. The findings reveal significant performance improvements, achieving 66.6{\%} accuracy on the FER2013 dataset. The quantitative results demonstrate the efficacy of our proposed SSL-based model in achieving accurate and robust facial expression recognition.",
isbn="978-981-99-7025-4"
}

@inproceedings{10.1007/978-3-031-56027-9_4,
author="Fei, Nanyi
and Jiang, Hao
and Lu, Haoyu
and Long, Jinqiang
and Dai, Yanqi
and Fan, Tuo
and Cao, Zhao
and Lu, Zhiwu",
editor="Goharian, Nazli
and Tonellotto, Nicola
and He, Yulan
and Lipani, Aldo
and McDonald, Graham
and Macdonald, Craig
and Ounis, Iadh",
title="VEMO: A Versatile Elastic Multi-modal Model for Search-Oriented Multi-task Learning",
booktitle="Advances in Information Retrieval",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="56--72",
abstract="Cross-modal search is one fundamental task in multi-modal learning, but there is hardly any work that aims to solve multiple cross-modal search tasks at once. In this work, we propose a novel Versatile Elastic Multi-mOdal (VEMO) model for search-oriented multi-task learning. VEMO is versatile because we integrate cross-modal semantic search, named entity recognition, and scene text spotting into a unified framework, where the latter two can be further adapted to entity- and character-based image search tasks. VEMO is also elastic because we can freely assemble sub-modules of our flexible network architecture for corresponding tasks. Moreover, to give more choices on the effect-efficiency trade-off when performing cross-modal semantic search, we place multiple encoder exits. Experimental results show the effectiveness of our VEMO with only 37.6{\%} network parameters compared to those needed for uni-task training. Further evaluations on entity- and character-based image search tasks also validate the superiority of search-oriented multi-task learning.",
isbn="978-3-031-56027-9"
}

@article{qin2023comprehensive,
  title={A comprehensive survey of artificial intelligence techniques for talent analytics},
  author={Qin, Chuan and Zhang, Le and Cheng, Yihang and Zha, Rui and Shen, Dazhong and Zhang, Qi and Chen, Xi and Sun, Ying and Zhu, Chen and Zhu, Hengshu and others},
  journal={arXiv preprint arXiv:2307.03195},
  year={2023}
}

@article{fabris2023fairness,
  title={Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey},
  author={Fabris, Alessandro and Baranowska, Nina and Dennis, Matthew J and Graus, David and Hacker, Philipp and Saldivar, Jorge and Zuiderveen Borgesius, Frederik and Biega, Asia J},
  journal={ACM Transactions on Intelligent Systems and Technology},
  year={2023},
  publisher={ACM New York, NY}
}

@inproceedings{mhadgut2022vrecruit,
  title={vRecruit: An Automated Smart Recruitment Webapp using Machine Learning},
  author={Mhadgut, Sanika and Koppikar, Neha and Chouhan, Nikhil and Dharadhar, Parag and Mehta, Parthak},
  booktitle={2022 International Conference on Innovative Trends in Information Technology (ICITIIT)},
  pages={1--6},
  year={2022},
  organization={IEEE}
}

@article{kathiravan2023modern,
  title={A modern online interview platform for recruitment system},
  author={Kathiravan, M and Madhurani, M and Kalyan, Sathya and Raj, Rahul and Jayan, Siddharth},
  journal={Materials Today: Proceedings},
  volume={80},
  pages={3022--3027},
  year={2023},
  publisher={Elsevier}
}

@article{aleisa2023implementing,
  title={Implementing AIRM: a new AI recruiting model for the Saudi Arabia labour market},
  author={Aleisa, Monirah Ali and Beloff, Natalia and White, Martin},
  journal={Journal of Innovation and Entrepreneurship},
  volume={12},
  number={1},
  pages={59},
  year={2023},
  publisher={Springer}
}

@inproceedings{signore2023digital,
  title={Digital job searching and recruitment platforms: A semi-systematic literature review},
  author={Signore, Chiara and Della Piana, Bice and Di Vincenzo, Francesco},
  booktitle={International Conference in Methodologies and intelligent Systems for Techhnology Enhanced Learning},
  pages={313--322},
  year={2023},
  organization={Springer}
}

@misc{fer2013_dataset,
    author = {Dumitru and Ian Goodfellow and Will Cukierski and Yoshua Bengio},
    title = {Challenges in Representation Learning: Facial Expression Recognition Challenge},
    year = {2013},
    howpublished = {\url{https://kaggle.com/competitions/challenges-in-representation-learning-facial-expression-recognition-challenge}},
    note = {Kaggle}
}

@article{fernandez2020ai,
  title={AI and recruiting software: Ethical and legal implications},
  author={Fern{\'a}ndez-Mart{\'\i}nez, Carmen and Fern{\'a}ndez, Alberto},
  journal={Paladyn, Journal of Behavioral Robotics},
  volume={11},
  number={1},
  pages={199--216},
  year={2020},
  publisher={De Gruyter}
}

@article{lukacik2022into,
  title={Into the void: A conceptual model and research agenda for the design and use of asynchronous video interviews},
  author={Lukacik, Eden-Raye and Bourdage, Joshua S and Roulin, Nicolas},
  journal={Human Resource Management Review},
  volume={32},
  number={1},
  pages={100789},
  year={2022},
  publisher={Elsevier}
}

@misc{hirevueAboutCompany,
	author = {},
	title = {{A}bout the {C}ompany | {L}eadership \& {C}{E}{O} | {H}ire{V}ue --- hirevue.com},
	howpublished = {\url{https://www.hirevue.com/about}},
	year = {},
	note = {[Accessed 29-10-2024]},
}

@article{putra2024mag,
  title={MAG-BERT-ARL for Fair Automated Video Interview Assessment},
  author={Putra, Bimasena and Azizah, Kurniawati and Mawalim, Candy Olivia and Hanif, Ikhlasul Akmal and Sakti, Sakriani and Leong, Chee Wee and Okada, Shogo},
  journal={IEEE Access},
  year={2024},
  publisher={IEEE}
}

@article{naim2016automated,
  title={Automated analysis and prediction of job interview performance},
  author={Naim, Iftekhar and Tanveer, Md Iftekhar and Gildea, Daniel and Hoque, Mohammed Ehsan},
  journal={IEEE Transactions on Affective Computing},
  volume={9},
  number={2},
  pages={191--204},
  year={2016},
  publisher={IEEE}
}

@inproceedings{avanish2022interview,
  title={Interview Performance Analysis using Emotion Detection},
  author={Avanish, KA and Aravind, H and Nair, Naveen P and Vivek, A and Anjali, T},
  booktitle={2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA)},
  pages={1424--1427},
  year={2022},
  organization={IEEE}
}

@inproceedings{goodfellow2013challenges,
  title={Challenges in representation learning: A report on three machine learning contests},
  author={Goodfellow, Ian J and Erhan, Dumitru and Carrier, Pierre Luc and Courville, Aaron and Mirza, Mehdi and Hamner, Ben and Cukierski, Will and Tang, Yichuan and Thaler, David and Lee, Dong-Hyun and others},
  booktitle={Neural information processing: 20th international conference, ICONIP 2013, daegu, korea, november 3-7, 2013. Proceedings, Part III 20},
  pages={117--124},
  year={2013},
  organization={Springer}
}

@InProceedings{10.1007/978-981-99-7025-4_26,
author="Hassan, Saif
and Ullah, Mohib
and Imran, Ali Shariq
and Cheikh, Faouzi Alaya",
editor="Liu, Fenrong
and Sadanandan, Arun Anand
and Pham, Duc Nghia
and Mursanto, Petrus
and Lukose, Dickson",
title="Attention-Guided Self-supervised Framework for Facial Emotion Recognition",
booktitle="PRICAI 2023: Trends in Artificial Intelligence",
year="2024",
publisher="Springer Nature Singapore",
address="Singapore",
pages="294--306",
abstract="Facial expression recognition is pivotal in computer vision and finds applications across various domains. In this paper, we proposed a self-supervised learning approach for precise facial expression recognition. Our approach leverages recent advancements in diffusion models, specifically the Classification and Regression Diffusion (CARD) model. To enhance the discriminative capability of our model, we integrate the Convolutional Block Attention Module (CBAM), an effective attention mechanism, to extract pertinent and discriminative feature maps. Furthermore, we capitalize on unlabelled data by using the simple contrastive learning framework of self-supervised learning (SSL) to extract meaningful features. To evaluate the performance, we conduct extensive experiments on the FER2013 dataset, comparing our results with existing benchmarks. The findings reveal significant performance improvements, achieving 66.6{\%} accuracy on the FER2013 dataset. The quantitative results demonstrate the efficacy of our proposed SSL-based model in achieving accurate and robust facial expression recognition.",
isbn="978-981-99-7025-4"
}
@article{BHAGAT20242079,
title = {Facial Emotion Recognition (FER) using Convolutional Neural Network (CNN)},
journal = {Procedia Computer Science},
volume = {235},
pages = {2079-2089},
year = {2024},
note = {International Conference on Machine Learning and Data Engineering (ICMLDE 2023)},
issn = {1877-0509},
doi = {https://doi.org/10.1016/j.procs.2024.04.197},
url = {https://www.sciencedirect.com/science/article/pii/S1877050924008731},
author = {Dhvanil Bhagat and Abhi Vakil and Rajeev Kumar Gupta and Abhijit Kumar},
keywords = {Facial Emotion Recognition (FER), Deep Convolution Neural Network (DCNN), Loss function, Adam, ReLu},
abstract = {Our research tackles the challenge of real-time facial emotion recognition (FER) from live video feeds, a crucial problem in various fields like security access, customer satisfaction analysis, and mental health diagnosis. Unlike previous studies focused on static images, our approach provides immediate emotion predictions directly from video streams. We implement Deep Convolutional Neural Networks (DCNN) for facial image classification. Moreover, we used pre-trained models like EfficientNet, ResNet, VGGNet and a Haar face classifier to achieve an impressive 82% accuracy on the training data of FER2013 dataset. This technology holds promise for human-computer interaction and mental health applications. However, it also raises ethical concerns, emphasizing the need for responsible deployment. Our work addresses the practical limitations of existing FER methods and bridges the gap between laboratory research and real-world applications, offering a valuable contribution to the field.}
}