Artículos
Más allá de la precisión: Confianza en el reconocimiento facial de emociones
Resumen
El reconocimiento facial de emociones ha avanzado gracias a redes neuronales convolucionales, al aprendizaje por transferencia, a modelos híbridos, a mecanismos de atención, a transformadores y a sistemas multimodales. Sin embargo, gran parte de la literatura sigue evaluando los modelos principalmente por su exactitud en un único conjunto de datos, lo que puede ocultar fallas de generalización, sesgos demográficos, ruido de etiquetas, sensibilidad a las perturbaciones y restricciones éticas para el despliegue. Este artículo presenta una revisión sistemática estructurada mediante PICOC y un protocolo PRISMA, basada en revisiones, conjuntos de datos públicos y modelos de aprendizaje profundo, correspondientes principalmente al periodo 2021– 2026. El objetivo es analizar cómo los enfoques recientes han equilibrado la exactitud en conjuntos de referencia con la validación cruzada entre dominios, el análisis de sesgo y la robustez en condiciones reales. En lugar de reproducir una clasificación centrada en la exactitud, se propone un marco de reconocimiento facial de emociones confiable que integra rendimiento, transferencia de dominio, equidad, explicabilidad, robustez y factibilidad computacional. Para operacionalizar ese marco sin reducir la confianza a una única puntuación universal, la matriz de evidencia de 38 fuentes se codifica en seis dominios de reporte y se resume mediante un índice de cobertura de evidencia de confiabilidad (Trustworthiness Evidence Coverage, 0–100 %), que mide la completitud de la evidencia que respalda una afirmación de confiabilidad y no la confiabilidad intrínseca de un modelo. La evidencia indica que la exactitud agregada es necesaria, pero insuficiente para sustentar reclamos de uso práctico.
Palabras clave
Cómo citar
Estadísticas
Referencias
Agrawal, I., Kumar, A., Swathi, D., Yashwanthi, V., & Hegde, R. (2021). Emotion recognition from facial expression using CNN. 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC), 1–6. https://doi.org/10.1109/R10-HTC53172.2021.9641578
Bahmanabady, E., Imani, M., & Ghassemian, H. (2023). Fusion of multi-level CNN with LBP features for facial emotion recognition. 2023 31st International Conference on Electrical Engineering (ICEE), 463–467. https://doi.org/10.1109/ICEE59167.2023.10334654
Banoth, S., Kukreja, V., Thapliyal, N., Aeri, M., & Sharma, R. (2024). Beyond basic emotions: High-accuracy facial expression classification using a CNN-SVM hybrid model. 2024 International Conference on Emerging Technologies in Computer Science for Interdisciplinary Applications (ICETCS), 1–6. https://doi.org/10.1109/ICETCS61022.2024.10544213
Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019). Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements. Psychological Science in the Public Interest, 20(1), 1–68. https://doi.org/10.1177/1529100619832930
Barsoum, E., Zhang, C., Ferrer, C. C., & Zhang, Z. (2016). Training deep networks for facial expression recognition with crowd-sourced label distribution. Proceedings of the 18th ACM International Conference on Multimodal Interaction, 279–283. https://doi.org/10.1145/2993148.2993165
Dominguez-Catena, I., Paternain, D., & Galar, M. (2022). Assessing demographic bias transfer from dataset to model: A case study in facial expression recognition. https://doi.org/10.48550/arXiv.2205.10049
Dominguez-Catena, I., Paternain, D., & Galar, M. (2024). Metrics for dataset demographic bias: A case study on facial expression recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(8), 5209–5226. https://doi.org/10.1109/TPAMI.2024.3361979
European Parliament and Council of the European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (artificial intelligence act). Official Journal of the European Union, L 2024/1689. https://eur-lex.europa.eu/eli/reg/2024/1689/oj
Gaya-Morey, F. X., Buades-Rubio, J. M., Palanque, P., Lacuesta, R., & Manresa-Yee, C. (2025). Deep learning-based facial expression recognition for the elderly: A systematic review. https://doi.org/10.48550/arXiv.2502.02618
Gaya-Morey, F. X., Manresa-Yee, C., Martinie, C., & Buades-Rubio, J. M. (2026). Evaluating facial expression recognition datasets for deep learning: A benchmark study with novel similarity metrics. IEEE Transactions on Affective Computing. https://doi.org/10.1109/TAFFC.2026.3693316
Georgiou, S., Psiris, A., Evangelatos, S., Lagkas, T., Argyriou, V., Sarigiannidis, P., Varlamis, I., & Papadopoulos, G. Th. (2026). Facial expression recognition in the deep learning era: A systematic multi-criteria review of methods, models, datasets, performance, challenges, and future research directions. https://doi.org/10.48550/arXiv.2606.08612
Goodfellow, I. J., Erhan, D., Carrier, P.-L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.-H., Zhou, Y., Ramaiah, C., Feng, F., Li, R., Wang, X., Athanasakis, D., Shawe-Taylor, J., Milakov, M., Park, J., & others. (2013). Challenges in representation learning: A report on three machine learning contests. arXiv. https://doi.org/10.48550/arXiv.1307.0414
Gupta, V., & Sejwar, V. (2022). Facial expression recognition with combination of geometric and textural domain features extractor using CNN and machine learning. 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), 400–407. https://doi.org/10.1109/ICAISS55157.2022.10010853
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90
Karatay, B., Bestepe, D., Sailunaz, K., Ozyer, T., & Alhajj, R. (2022). A multimodal emotion recognition system based on CNN-transformer deep learning technique. 2022 7th International Conference on Data Science and Machine Learning Applications (CDMA), 145–150. https://doi.org/10.1109/CDMA54072.2022.00029
Khaireddin, Y., & Chen, Z. (2021). Facial emotion recognition: State of the art performance on FER2013. https://doi.org/10.48550/arXiv.2105.03588
Khajuria, O., Kumar, R., & Gupta, M. (2023). Facial emotion recognition using CNN and VGG-16. 2023 International Conference on Inventive Computation Technologies (ICICT), 472–477. https://doi.org/10.1109/ICICT57646.2023.10133972
Kim, J.-H., Kim, N., & Won, C. S. (2022). Facial expression recognition with swin transformer. https://doi.org/10.48550/arXiv.2203.13472
Li, S., & Deng, W. (2022). Deep facial expression recognition: A survey. IEEE Transactions on Affective Computing, 13(3), 1195–1215. https://doi.org/10.1109/TAFFC.2020.2981446
Li, S., Deng, W., & Du, J. (2017). Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2584–2593. https://doi.org/10.1109/CVPR.2017.277
Li, T.-H. S., Kuo, P.-H., Tsai, T.-N., & Luan, P.-C. (2019). CNN and LSTM based facial expression analysis model for a humanoid robot. IEEE Access, 7, 93998–94011. https://doi.org/10.1109/ACCESS.2019.2928364
Lian, T., & Celiktutan, O. (2025). A feature-level bias evaluation framework for facial expression recognition models. https://doi.org/10.48550/arXiv.2505.20512
Lian, Z., Sun, H., Sun, L., Wen, Z., Zhang, S., Chen, S., Gu, H., Zhao, J., Ma, Z., Chen, X., Yi, J., Liu, R., Xu, K., Liu, B., Cambria, E., Zhao, G., Schuller, B. W., & Tao, J. (2024). MER 2024: Semi-supervised learning, noise robustness, and open-vocabulary multimodal emotion recognition. Proceedings of the 2nd International Workshop on Multimodal and Responsible Affective Computing, 41–48. https://doi.org/10.1145/3689092.3689959
Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010). 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, 94–101. https://doi.org/10.1109/CVPRW.2010.5543262
Lyons, M. J., Akamatsu, S., Kamachi, M., & Gyoba, J. (1998). Coding facial expressions with Gabor wavelets. Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, 200–205. https://doi.org/10.1109/AFGR.1998.670949
Mao, J., Xu, R., Yin, X., Chang, Y., Nie, B., & Huang, A. (2025). POSTER++: A simpler and stronger facial expression recognition network. Pattern Recognition, 157, 110951. https://doi.org/10.1016/j.patcog.2024.110951
Mohammad, S. M. (2022). Ethics sheet for automatic emotion recognition and sentiment analysis. Computational Linguistics, 48(2), 239–278. https://doi.org/10.1162/coli_a_00433
Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2019). AffectNet: A database for facial expression, valence, and arousal computing in the wild. IEEE Transactions on Affective Computing, 10(1), 18–31. https://doi.org/10.1109/TAFFC.2017.2740923
Negi, A. S., Arora, A., Bisht, S., Devliyal, S., Kumar, B. V., & Kaur, G. (2024). Facial emotion detection using CNN and VGG16 model. 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), 1–5. https://doi.org/10.1109/I2CT61223.2024.10543618
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Poulose, A., Kim, J.-H., & Han, D. S. (2021). Feature vector extraction technique for facial emotion recognition using facial landmarks. 2021 International Conference on Information and Communication Technology Convergence (ICTC), 1072–1076. https://doi.org/10.1109/ICTC52510.2021.9620798
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 33–44. https://doi.org/10.1145/3351095.3372873
Rawat, U., & Rai, C. S. (2023). Improving facial emotion recognition through transfer learning with deep convolutional neural network models. 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS), 1335–1339. https://doi.org/10.1109/ICTACS59847.2023.10390186
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4510–4520. https://doi.org/10.1109/CVPR.2018.00474
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. 2017 IEEE International Conference on Computer Vision (ICCV), 618–626. https://doi.org/10.1109/ICCV.2017.74
Sharafi, M., Yazdchi, M., & Rasti, J. (2023). Audio-visual emotion recognition using k-means clustering and spatio-temporal CNN. 2023 6th International Conference on Pattern Recognition and Image Analysis (IPRIA), 1–6. https://doi.org/10.1109/IPRIA59240.2023.10147192
Shirsath, S., Vikhe, V., & Vikhe, P. (2022). Xception CNN-ensemble learning based facial emotion recognition. 2022 6th International Conference on Computing, Communication, Control and Automation (ICCUBEA), 1–4. https://doi.org/10.1109/ICCUBEA54992.2022.10011111
Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., & Zhang, W. (2022). A systematic review on affective computing: Emotion models, databases, and recent advances. Information Fusion, 83–84, 19–52. https://doi.org/10.1016/j.inffus.2022.03.009
Zheng, C., Mendieta, M., & Chen, C. (2023). POSTER: A pyramid cross-fusion transformer network for facial expression recognition. 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 3138–3147. https://doi.org/10.1109/ICCVW60793.2023.00339
