A Study on the Performance of Unconstrained Very Low Resolution Face Recognition: Analyzing Current Trends and New Research Directions

• Published in: IEEE access Vol. 9; pp. 75470 - 75493
• Main Authors: Luevano, Luis S., Chang, Leonardo, Heydi Mendez-Vazquez, Heydi, Martinez-Diaz, Yoanna, Gonzalez-Mendoza, Miguel
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Context; coupled mappings; Deep learning; Domains; efficient face recognition models; Face recognition; Facial recognition technology; Feature extraction; Image resolution; lightweight convolutional neural networks; Low resolution face recognition; Machine learning; Neural networks; Probes; super resolution; Surveillance; Task analysis; unconstrained face recognition
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3080712

In the past decade, research in the face recognition area has advanced tremendously, particularly in uncontrolled scenarios (face recognition in the wild). This advancement has been achieved partly due to the massive popularity and effectiveness of deep convolutional neural networks and the availability of larger unconstrained datasets. However, several face recognition challenges remain in the context of very low resolution homogeneous (same domain) and heterogeneous (different domain) face recognition. In this survey, we study the seminal and novel methods to tackle the very low resolution face recognition problem and provide an in-depth analysis of their design, effectiveness, and efficiency for a real-time surveillance application. Furthermore, we analyze the advantage of employing deep learning convolutional neural networks, while presenting future research directions for effective deep learning network design in this context.