Niil IA
A Niil IA é uma ferramenta inovadora que está revolucionando o processo de recrutamento e seleção.
É importante ressaltar que a medição das expressões faciais deve ser realizada de forma ética e respeitando a privacidade dos candidatos. Os recrutadores devem ser treinados adequadamente no uso de tecnologias de reconhecimento facial e garantir que a análise das expressões faciais seja apenas um dos vários critérios considerados ao avaliar os candidatos.
Com um avançado algoritmo de inteligência artificial, a Niil IA é capaz de analisar as expressões faciais dos candidatos durante o vídeo currículo, fornecendo insights valiosos sobre suas emoções. Com essa tecnologia, é possível detectar até sete emoções distintas, permitindo que os recrutadores tenham uma compreensão mais completa e precisa do perfil emocional de cada candidato.
A análise das expressões faciais é uma técnica poderosa para compreender as reações emocionais de uma pessoa. A Niil IA utiliza uma combinação de reconhecimento facial para identificar e interpretar 7 emoções: desconforto, satisfação, animosidade, cautela, surpresa, neutralidade e temor.
Com o Niil IA, os processos seletivos tornam-se mais eficientes e eficazes. A ferramenta oferece aos recrutadores uma avaliação objetiva e imparcial das emoções do candidato, ajudando a identificar pessoas com maior adequação à cultura organizacional, capacidade de lidar com situações de estresse e habilidades de comunicação interpessoal, fornecendo aos recrutadores uma visão mais completa e precisa dos candidatos.
Com essa tecnologia, as empresas podem tomar decisões de contratação mais embasadas, resultando em equipes mais alinhadas emocionalmente e, consequentemente, em um ambiente de trabalho mais produtivo e harmonioso.
No entanto, é necessário realizar uma análise conjunta com profissionais da Psicologia, incorporando testes comportamentais inovadores e relacionando-os com o contexto atual do mercado de trabalho.
A Niil IA é uma ferramenta inovadora que traz uma nova dimensão ao processo de recrutamento!
Ao utilizar a Niil IA, nosso cliente terá acesso a um gráfico detalhado que representa as diferentes emoções do candidato. Essa visualização gráfica permitirá uma análise mais aprofundada de cada uma dessas emoções. Com essa ferramenta, será possível identificar e compreender melhor as nuances emocionais do candidato, auxiliando na tomada de decisões mais informadas. O gráfico proporciona uma visão clara e objetiva das emoções expressas, tornando o processo de análise mais preciso e eficiente. Com essas informações em mãos, nosso cliente poderá avaliar de forma mais completa e assertiva cada um dos candidatos.
Base científica da nossa Inteligência Artificial Niil IA:
- Darwin, C. "The Expression of the Emotions in Man and Animals." (1886).
- Ekman, P. "Facial expression and emotion." Am. Psychol. 48, 384 (1993).
- Ekman, P. & Friesen, W. "Facial action coding system: a technique for the measurement of facial movement." Palo Alto: Consulting Psychologists (1978).
- Cohn, J. F., Ambadar, Z. & Ekman, P. "Observer-based measurement of facial expression with the Facial Action Coding System." The handbook of emotion elicitation and assessment, 203–221 (2007).
- Kilbride, J. E. & Yarczower, M. "Ethnic bias in the recognition of facial expressions." J. Nonverbal Behav. 8, 27–41 (1983).
- Graesser, A. C. et al. "Detection of emotions during learning with AutoTutor." Proceedings of the 28th annual meetings of the cognitive science society, 285–290 (Citeseer, 2006).
- Fridlund, A. J., Schwartz, G. E. & Fowler, S. C. "Pattern recognition of self-reported emotional state from multiple-site facial EMG activity during affective imagery." Psychophysiology 21, 622–637 (1984).
- Larsen, J. T., Norris, C. J. & Cacioppo, J. T. "Effects of positive and negative affect on electromyographic activity over zygomaticus major and corrugator supercilii." Psychophysiology 40, 776–785 (2003).
- Sayette, M. A. et al. "Alcohol and group formation: a multimodal investigation of the effects of alcohol on emotion and social bonding." Psychol. Sci. 23, 869–878 (2012).
- Navarathna, R. et al. "Predicting movie ratings from audience behaviors." IEEE Winter Conference on Applications of Computer Vision, 1058–1065 (2014).
- Golland, Y., Mevorach, D. & Levit-Binnun, N. "Affiliative zygomatic synchrony in co-present strangers." Scientific Reports vol. 9 (2019).
- Cheong, J. H., Brooks, S. & Chang, L. J. "FaceSync: Open source framework for recording facial expressions with head-mounted cameras." F1000Res. (2019).
- Cheong, J. H., Molani, Z., Sadhukha, S. & Chang, L. J. "Synchronized affect in shared experiences strengthens social connection." (2020) doi:10.31234/osf.io/bd9wn.
- De la Torre, F. et al. "IntraFace." IEEE Int Conf Autom Face Gesture Recognit Workshops 1, (2015).
- Vemulapalli, R. & Agarwala, A. "A compact embedding for facial expression similarity." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5683–5692 (openaccess.thecvf.com, 2019).
- Stöckli, S., Schulte-Mecklenbeck, M., Borer, S. & Samson, A. C. "Facial expression analysis with AFFDEX and FACET: A validation study." Behav. Res. Methods 50, 1446–1460 (2018).
- Haines, N., Southward, M. W., Cheavens, J. S., Beauchaine, T. & Ahn, W.-Y. "Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity." PLoS One 14, e0211735 (2019).
- Höfling, T. T. A., Gerdes, A. B. M., Föhl, U. & Alpers, G. W. "Read My Face: Automatic Facial Coding Versus Psychophysiological Indicators of Emotional Valence and Arousal." Front. Psychol. 11, 1388 (2020).
- Dupré, D., Krumhuber, E. G., Küster, D. & McKeown, G. J. "A performance comparison of eight commercially available automatic classifiers for facial affect recognition." PLoS One 15, e0231968 (2020).
- Werner, P. et al. "Automatic Pain Assessment with Facial Activity Descriptors." IEEE Transactions on Affective Computing 8, 286–299 (2017).
- Chen, P.-H. A. et al. "Socially transmitted placebo effects." Nat Hum Behav 3, 1295–1305 (2019).
- Littlewort, G. C., Bartlett, M. S. & Lee, K. Automatic coding of facial expressions displayed during posed and genuine pain. Image Vis. Comput. 27, 1797–1803 (2009).
- Wang, Y. et al. Automatic Depression Detection via Facial Expressions Using Multiple Instance Learning. in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) 1933–1936 (2020).
- Penton-Voak, I. S., Pound, N., Little, A. C. & Perrett, D. I. Personality Judgments from Natural and Composite Facial Images: More Evidence For A ‘Kernel Of Truth’ In Social Perception. Soc. Cogn. 24, 607–640 (2006).
- Segalin, C. et al. What your Facebook Profile Picture Reveals about your Personality. Proceedings of the 25th ACM international conference on Multimedia (2017) doi:10.1145/3123266.3123331.
- Kachur, A., Osin, E., Davydov, D., Shutilov, K. & Novokshonov, A. Assessing the Big Five personality traits using real-life static facial images. Sci. Rep. 10, 8487 (2020).
- Kosinski, M. Facial recognition technology can expose political orientation from naturalistic facial images. Sci. Rep. 11, 100 (2021).
- Kanade, T., Cohn, J. F. & Yingli Tian. Comprehensive database for facial expression analysis. in Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580) 46–53 (2000).
- Lucey, P. et al. The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops (2010) doi:10.1109/cvprw.2010.5543262.
- Mavadati, S. M., Mahoor, M. H., Bartlett, K., Trinh, P. & Cohn, J. F. DISFA: A Spontaneous Facial Action Intensity Database. IEEE Transactions on Affective Computing 4, 151–160 (2013).
- Zhang, X. et al. BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database. Image Vis. Comput. 32, 692–706 (2014).
- Mavadati, M., Sanger, P. & Mahoor, M. H. Extended disfa dataset: Investigating posed and spontaneous facial expressions. in proceedings of the IEEE conference on computer vision and pattern recognition workshops 1–8 (2016).
- Zhang, Z. et al. Multimodal spontaneous emotion corpus for human behavior analysis. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 3438–3446 (2016).
- Krumhuber, E. G., Skora, L., Küster, D. & Fou, L. A Review of Dynamic Datasets for Facial Expression Research. Emot. Rev. 9, 280–292 (2017).
- Dhall, A., Goecke, R., Joshi, J., Sikka, K. & Gedeon, T. Emotion Recognition In The Wild Challenge 2014: Baseline, Data and Protocol. in Proceedings of the 16th International Conference on Multimodal Interaction 461–466 (Association for Computing Machinery, 2014).
- Qi, P., Zhang, Y., Zhang, Y., Bolton, J. & Manning, C. D. Stanza: A Python Natural Language Processing Toolkit for Many Human Languages. arXiv [cs.CL] (2020).
- Brockman, G. et al. OpenAI Gym. arXiv [cs.LG] (2016).
- iMotions Biometric Research Platform 6.0. (iMotions A/S, Copenhagen, Denmark, 2016).
- Van Kuilenburg, H., Den Uyl, M. J., Israël, M. L. & Ivan, P. Advances in face and gesture analysis. Measuring Behavior 2008 371, (2008).
- Yitzhak, N. et al. Gently does it: Humans outperform a software classifier in recognizing subtle, nonstereotypical facial expressions. Emotion 17, 1187–1198 (2017).
- Krumhuber, E. G., Küster, D., Namba, S. & Skora, L. Human and machine validation of 14 databases of dynamic facial expressions. Behav. Res. Methods (2020) doi:10.3758/s13428-020-01443-y.
- Krumhuber, E. G., Küster, D., Namba, S., Shah, D. & Calvo, M. G. Emotion recognition from posed and spontaneous dynamic expressions: Human observers versus machine analysis. Emotion 21, 447–451 (2021).
- Littlewort, G. et al. The computer expression recognition toolbox (CERT). in 2011 IEEE International Conference on Automatic Face Gesture Recognition (FG) 298–305 (ieeexplore.ieee.org, 2011).
- McDuff, D. et al. AFFDEX SDK: A Cross-Platform Real-Time Multi-Face Expression Recognition Toolkit. in Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems 3723–3726 (Association for Computing Machinery, 2016).
- Baltrusaitis, T., Zadeh, A., Lim, Y. C. & Morency, L. OpenFace 2.0: Facial Behavior Analysis Toolkit. in 2018 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018) 59–66 (2018).
- Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W. & Smith, S. M. FSL. Neuroimage 62, 782–790 (2012).
- Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173 (1996).
- Friston, K. J., Frith, C. D., Liddle, P. F. & Frackowiak, R. S. Comparing functional (PET) images: the assessment of significant change. J. Cereb. Blood Flow Metab. 11, 690–699 (1991).
- Abraham, A. et al. Machine learning for neuroimaging with scikit-learn. Front. Neuroinform. 8, 14 (2014).
- Ekman, P. & Rosenberg, E. L. What the Face Reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS). (Oxford University Press, 1997).
- Paszke, A. et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. arXiv [cs.LG] (2019).
- Pedregosa, F. et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
- Zhang, S. et al. FaceBoxes: A CPU real-time face detector with high accuracy. in 2017 IEEE International Joint Conference on Biometrics (IJCB) 1–9 (2017).
- Zhang, L. et al. Multi-Task Cascaded Convolutional Networks Based Intelligent Fruit Detection for Designing Automated Robot. IEEE Access 7, 56028–56038 (2019).
- Zhang, N., Luo, J. & Gao, W. Research on Face Detection Technology Based on MTCNN. in 2020 International Conference on Computer Network, Electronic and Automation (ICCNEA) 154–158 (2020).
- Deng, J. et al. RetinaFace: Single-stage Dense Face Localisation in the Wild. arXiv [cs.CV] (2019).
- Yang, S., Luo, P., Loy, C.-C. & Tang, X. Wider face: A face detection benchmark. in Proceedings of the IEEE conference on computer vision and pattern recognition 5525–5533 (2016).
- Shen, J. et al. The first facial landmark tracking in-the-wild challenge: Benchmark and results. in Proceedings of the IEEE international conference on computer vision workshops 50–58 (2015).
- Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S. & Pantic, M. 300 Faces In-The-Wild Challenge: database and results. Image Vis. Comput. 47, 3–18 (2016).
- Guo, X. et al. PFLD: A Practical Facial Landmark Detector. arXiv [cs.CV] (2019).
- Howard, A. G. et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv [cs.CV] (2017).
- Chen, S., Liu, Y., Gao, X. & Han, Z. MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices. arXiv [cs.CV] (2018).
- Sagonas, C., Tzimiropoulos, G., Zafeiriou, S. & Pantic, M. A semi-automatic methodology for facial landmark annotation. in Proceedings of the IEEE conference on computer vision and pattern recognition workshops 896–903 (2013).
- Hyniewska, S., Sato, W., Kaiser, S. & Pelachaud, C. Naturalistic Emotion Decoding From Facial Action Sets. Front. Psychol. 9, 2678 (2018).
- Grafsgaard, J. F., Boyer, K. E. & Lester, J. C. Predicting Facial Indicators of Confusion with Hidden Markov Models. in Affective Computing and Intelligent Interaction 97–106 (Springer Berlin Heidelberg, 2011).
- Julle-Danière, E. et al. Are there non-verbal signals of guilt? PLoS One 15, e0231756 (2020).
- Shao, Z., Liu, Z., Cai, J. & Ma, L. Deep adaptive attention for joint facial action unit detection and face alignment. in Proceedings of the European Conference on Computer Vision (ECCV) 705–720 (2018).
- Shao, Z., Liu, Z., Cai, J. & Ma, L. JÂA-net: Joint facial action unit detection and face alignment via adaptive attention. Int. J. Comput. Vis. 129, 321–340 (2021).
- Li et al. An EEG-Based Multi-Modal Emotion Database with Both Posed and Authentic Facial Actions for Emotion Analysis. in 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) (FG) vol. 0 336–343 (2020).
- Lucey, P., Cohn, J. F., Prkachin, K. M., Solomon, P. E. & Matthews, I. Painful data: The UNBC-McMaster shoulder pain expression archive database. in 2011 IEEE International Conference on Automatic Face Gesture Recognition (FG) 57–64 (2011).
- Kollias, D., Nicolaou, M. A., Kotsia, I., Zhao, G. & Zafeiriou, S. Recognition of affect in the wild using deep neural networks. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops 26–33 (2017).
- Zafeiriou, S. et al. Aff-Wild: Valence and Arousal ‘In-the-Wild’ Challenge. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2017).
- Kollias, D. et al. Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond. Int. J. Comput. Vis. 127, 907–929 (2019).
- Kollias, D. & Zafeiriou, S. Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace. arXiv [cs.CV] (2019).
- Kollias, D., Sharmanska, V. & Zafeiriou, S. Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network. arXiv [cs.CV] (2019).
- Kollias, D., Schulc, A., Hajiyev, E. & Zafeiriou, S. Analysing Affective Behavior in the First ABAW 2020 Competition. arXiv [cs.LG] (2020).
- Baltrušaitis, T., Mahmoud, M. & Robinson, P. Cross-dataset learning and person-specific normalisation for automatic Action Unit detection. in 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) vol. 06 1–6 (2015).
- Dalal, N. & Triggs, B. Histograms of oriented gradients for human detection. in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) vol. 1 886–893 vol. 1 (2005).
- van der Walt, S. et al. scikit-image: image processing in Python. PeerJ 2, e453 (2014).
- Pedregosa, F. et al. Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12, 2825–2830 (2011).
- Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M. & Pollak, S. D. Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements.
Psychological Science in the Public Interest vol. 20 1–68 (2019).
- Haines, N. et al. Regret Induces Rapid Learning from Experience-based Decisions: A Model-based Facial Expression Analysis Approach. bioRxiv 560011 (2019) doi:10.1101/560011.
- Luan, P., Huynh, V. & Tuan Anh, T. Facial Expression Recognition using Residual Masking Network. in IEEE 25th International Conference on Pattern Recognition 4513–4519 (2020).
- Goodfellow, I. J. et al. Challenges in representation learning: A report on three machine learning contests. Neural Networks vol. 64 59–63 (2015).
- Zhang, Z., Luo, P., Loy, C. C. & Tang, X. From facial expression recognition to interpersonal relation prediction. Int. J. Comput. Vis. 126, 550–569 (2018).
- Lyons, M., Kamachi, M. & Gyoba, J. The Japanese Female Facial Expression (JAFFE) Dataset. (1998). doi:10.5281/zenodo.3451524.
- Mollahosseini, A., Hasani, B. & Mahoor, M. H. AffectNet: A database for facial expression, valence, and arousal computing in the wild. IEEE Trans. Affect. Comput. 10, 18–31 (2019).
- McKinney, W. & Others. pandas: a foundational Python library for data analysis and statistics. Python for High Performance and Scientific Computing 14, 1–9 (2011).
- Afzal, S. & Robinson, P. Natural affect data — Collection & annotation in a learning context. 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops (2009) doi:10.1109/acii.2009.5349537.
- Jones, E., Oliphant, T., Peterson, P. & Others. SciPy: Open source scientific tools for Python. (2001).
- Chang, L. et al. cosanlab/nltools: 0.3.14. (Zenodo, 2019). doi:10.5281/ZENODO.2229812.
- Fabian Benitez-Quiroz, C., Srinivasan, R. & Martinez, A. M. Emotionet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild. in Proceedings of the IEEE conference on computer vision and pattern recognition 5562–5570 (2016).
- Zhu, Y., Cai, H., Zhang, S., Wang, C. & Xiong, Y. TinaFace: Strong but Simple Baseline for Face Detection. arXiv [cs.CV] (2020).
- Jourabloo, A., Ye, M., Liu, X. & Ren, L. Pose-invariant face alignment with a single cnn. in Proceedings of the IEEE International Conference on computer vision 3200–3209 (2017).
- Zhu, X., Lei, Z., Liu, X., Shi, H. & Li, S. Z. Face alignment across large poses: A 3d solution. in Proceedings of the IEEE conference on computer vision and pattern recognition 146–155 (2016).
- Valle, R., Buenaposada, J. M., Valdés, A. & Baumela, L. Face alignment using a 3D deeply-initialized ensemble of regression trees. Computer Vision and Image Understanding vol. 189 102846 (2019).
- Valle, R., Buenaposada, J. M., Valdes, A. & Baumela, L. A deeply-initialized coarse-to-fine ensemble of regression trees for face alignment. in Proceedings of the European Conference on Computer Vision (ECCV) 585–601 (2018).
- Watson, D. M., Brown, B. B. & Johnston, A. A data-driven characterisation of natural facial expressions when giving good and bad news. PLoS Comput. Biol. 16, e1008335 (2020).
- Chang, L. J. et al. Endogenous variation in ventromedial prefrontal cortex state dynamics during naturalistic viewing reflects affective experience. bioRxiv (2018).
- Jack, R. E., Garrod, O. G. B. & Schyns, P. G. Dynamic facial expressions of emotion transmit an evolving hierarchy of signals over time. Curr. Biol. 24, 187–192 (2014).
- Rhue, L. Racial Influence on Automated Perceptions of Emotions. (2018) doi:10.2139/ssrn.3281765.
- Nagpal, S., Singh, M., Singh, R. & Vatsa, M. "Deep Learning for Face Recognition: Pride or Prejudiced?" arXiv [cs.CV] (2019).
- McDuff, D., Gontarek, S. & Picard, R. "Remote Measurement of Cognitive Stress via Heart Rate Variability." Conf. Proc. IEEE Eng. Med. Biol. Soc. 2014, 2957–2960 (2014).