Open research questions in Face recognition and analysis
105 unresolved questions extracted from the limitations and future-work sections of 500 Face recognition and analysis papers in our library. Each links back to the study that raised it.
What the literature leaves open
Finally, we discuss the open challenges and potential future work that need to be addressed in the field of biometrics in XR.
Conventional surveillance methods are no longer sufficient in the modern world's fast-changing security environment. There is a need for systems that can evaluate behavior to differentiate between normal and abnormal behavior.
Real-Time Edge-Based Burglary Detection and Automated Alerting Using Deep Learning Framework · 2026 · DOIThe ethical risks posed by some uses of facial analysis AI. The potential for discrimination and oppression. The need for stricter definitions of pseudotechnology.
The lack of a universally accepted definition of pseudoscience. The need for a more accurate tool for analyzing the epistemic problems of facial analysis AI.
The drag-and-drop sketch construction interface is described qualitatively as requiring no artistic expertise, but no user study data, error rates for untrained users, or comparison of sketch quality produced by forensic artists versus novice users is provided to validate usability claims.
No ablation studies or comparative validation are provided to determine which deep learning architecture components (feature encoders, generative adversarial networks, domain alignment) contribute most to the reported 90% accuracy in sketch-photo synthesis and recognition.
The study is limited by the time and resource constraints of applying NST to large datasets. The reduction of the CatFLW dataset to a smaller subset of 600 images may affect the generalizability of the results.
Supervised Neural Style Transfer as an Augmentation Technique for Facial Landmark Detection · 2026 · DOIThe scarcity of field-specific data is a significant problem in data science. There is a need for effective data augmentation techniques for landmark detection in the animal domain.
Supervised Neural Style Transfer as an Augmentation Technique for Facial Landmark Detection · 2026 · DOIRecent studies reveal demographic performance disparities that existing methods have not adequately addressed. Fairness approaches often fail to generalize under distribution shifts.
AI-generated face detection fairness evaluation is currently limited to facial imagery. Future work should extend fairness competitions to broader visual contexts and explicitly measure performance trade-offs between faces and other image types to create more challenging and realistic evaluation scenarios.
Traditional attendance methods are inefficient, time-consuming, and prone to proxy attendance. There is a need for an automated solution that can improve efficiency, scalability, and reliability.
Furthermore, hardware as lowresolution cameras or limited processing power can affect system efficiency. High-quality input data and adequate computational resources are necessary to achieve optimal performance. such © 2026 The Author(s). Published by IJCOPE Journal. Website: https://ijcope.org/ 6 International Journal of Creative and Open Research in Engineering and Management ISSN: 3108-1754 (Online) Volume 02 Issue 05 May-2026 | Impact Factor: 3.5 related Overall, these challenges highlight the need for continuous improvement in system design. Addressing issues illumination, occlusion, pose variation, and computational efficiency can further enhance the robustness and reliability of the proposed attendance system.
The paper identifies challenges such as constrained receptive fields, high computational costs, and the need for extensive data. The survey also discusses the challenge of evaluating the quality of restored face videos.
The paper identifies a gap in deep learning-based face video restoration methods, as no comprehensive survey has systematically reviewed these methods. The survey aims to bridge this gap by providing a comprehensive review of existing methods.
Using multi-modal information to enhance the stability of all-weather recognition. Creating lightweight networks to implement embedded systems. Using meta-learning and domain adaptation to enhance cross-scene generalization.
The gap between the performance of face recognition algorithms in controlled environments and real-world scenarios. The need for more robust face recognition algorithms that can handle varying lighting conditions and occlusions.
The system may not perform well in situations with strong pose variations, occlusions, and uneven lighting. The system requires a limited set of candidate windows to survive the Viola-Jones cascade.
A quality-gated hybrid Viola–Jones pipeline for efficient face detection under computational constraints · 2026 · DOIThe model is limited by the quality of the training data. The model may not generalize well to new, unseen data. The model is vulnerable to adversarial attacks.
1) Deepfake detection continues to develop thanks to improvements in artificial intelligence and security technologies. In particular, nowadays, researchers do not Volume 14 Issue 5, May 2026 www.ijser.in Licensed Under Creative Commons Attribution CC BY Paper ID: SE26508232113DOI: https://dx.doi.org/10.70729/SE2650823211327 of 28 International Journal of Scientific Engineering and Research (IJSER) ISSN (Online): 2347-3878 SJIF (2025): 8.036 on the generalization problem, showing that many detection models struggle when tested on unseen datasets. (ScienceDirect) Bharati, N. (2025). Explainable deepfake detection framework. This research introduces explainable AI (XAI) techniques in deepfake detection, making results more interpretable for forensic and legal applications. (ScienceDirect) Khan, A. A. et al. (2025). Survey on multimediaenabled deepfake detection. The paper discusses multimodal detection systems combining video, audio, and facial cues to improve accuracy and robustness. (Springer) Qureshi, S. M. et al. (2024). Survey of digital forensic methods for multimodal deepfake detection. This study explores detection across multiple media types and highlights challenges in detecting deepfake content on social media platforms. (PMC) Gupta, G. (2023). Deepfake detection using multimodal and machine learning approaches. The research into datasets, benchmarks, and provides machine learning techniques used in deepfake detection systems. (MDPI) insights Wang, T. (2022). Deepfake detection: A reliabilityfocused survey. This paper emphasizes challenges such as robustness, transferability, and interpretability in detection models. (arXiv) ResearchGate Survey (2025). A survey on deepfake video detection. This review highlights the current state of deepfake detection and stresses the need for better real-world applicability and robustness. (ResearchGate) Banerjee, S. (2025). Survey on deepfake detection technologies. The paper examines both traditional and modern detection methods, including biometric and AIbased techniques. (ResearchGate) IJRCSEIT (2026). Deepfake detection through deep learning. This study compares different neural network architectures such as CNNs, RNNs, and transformers in terms of performance and efficiency. (IJSRCSEIT) Singh, S. (2025). Integrative review of deepfake detection and multimedia forensics. The authors discuss the social and cybersecurity implications of deepfakes and suggest interdisciplinary solutions. (PMC) limit themselves to studying the number of blinks; instead, they try to develop a system capable of recognizing changes and adapting to them. it is important 2) Multimodal biometric techniques do not imply only facial features; to analyze audio characteristics in addition. Today's security technologies use not only face recognition and verification but also fingerprint, voice, and multifactor authentications. 3) Thanks to the development of real-time detection technology, it becomes possible to prevent attempts to post any deepfake videos online instantly. In addition, blockchain technology should be regarded as promising within this area. 4) Finally, the role of datasets increases since face detection models prepare fight with deepfakes. the Simultaneously, detection tools are improving, and privacy laws are introduced.
Traditional camera-based systems have privacy challenges and environmental constraints. Ear-worn devices require enhancements in design appeal and energy efficiency.
The mismatch between training data distribution and real deployment scenarios. The misalignment between model feature capacity allocation and business priority. The lack of efficient and stable face detection models under resource-constrained conditions.
Scale-Aligned Capacity Allocation: A Lightweight Face Detection Framework for Fixed-View Unmanned Restaurant Scenarios · 2026 · DOIThe identification of age and gender from facial images is difficult due to variations in lighting conditions and facial poses. Inaccurate predictions can have consequences in surveillance systems and social and demographic studies.
The lack of a formal framework for allocating non-colliding biometric identities to digital entities. The need for a technique to provision digital entities with unique biometric identities.
Non-Colliding Biometric Identities for Digital Entities: Geometry, Capacity, and Million-Scale Virtual Identity Provisioning · 2026Current attendance management systems are slow, cumbersome, and susceptible to errors. Face recognition systems have shortcomings, including poor performance in real-world scenarios.
Exploring the use of other modalities, such as audio, to enhance deepfake detection. Investigating the application of the proposed framework to other domains, such as image manipulation detection.
Robust deepfake video detection using spatio-temporal features and dynamic difference learning · 2026 · DOI
Most-cited papers in Face recognition and analysis
- Presentation and validation of the Radboud Faces Database · Cognition & Emotion · 2010 · 2,022 citations
- Recognition for faces of own and other race. · Journal of Personality and Social Psychology · 1969 · 520 citations
- Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. · Journal of Personality and Social Psychology · 2018 · 408 citations
- YOLO-FaceV2: A scale and occlusion aware face detector · Pattern Recognition · 2024 · 372 citations
- AI-synthesized faces are indistinguishable from real faces and more trustworthy · Proceedings of the National Academy of Sciences · 2022 · 334 citations
- Robust representations for face recognition: The power of averages · Cognitive Psychology · 2005 · 242 citations
- A review of the literature on the aging adult skull and face: Implications for forensic science research and applications · Forensic Science International · 2007 · 242 citations
- Recognizing one's own face · Cognition · 2001 · 234 citations
- Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attention · ACM Transactions on Multimedia Computing Communications and Applications · 2022 · 212 citations
- Dynamic smile visualization and quantification: Part 1. Evolution of the concept and dynamic records for smile capture · American Journal of Orthodontics and Dentofacial Orthopedics · 2003 · 202 citations
Most recent work
- Training humans to detect AI-generated faces · Proceedings of the National Academy of Sciences · 2026
- Is this real? Susceptibility to deepfakes in machines and humans · Cognitive Research Principles and Implications · 2026
- Enhancing human detection of real and hyperrealistic AI-generated faces · Computers in Human Behavior · 2026
- A new automated 3d facial soft tissue landmarking method via deep learning · Journal of Dentistry · 2026
- Enhancing face mask detection performance using stacked neural ensembles in mask fusion · Discover Artificial Intelligence · 2026
- An efficient hybrid CNN–transformer framework for real-time weapon detection and face recognition · Frontiers in Artificial Intelligence · 2026
- Real-Time Edge-Based Burglary Detection and Automated Alerting Using Deep Learning Framework · International Research Journal on Advanced Engineering and Management (IRJAEM) · 2026
- Facial analysis AI as social pseudotechnology · European Journal for Philosophy of Science · 2026
- Criminal Face Sketch Recognition and Construction · International Journal for Research in Applied Science and Engineering Technology · 2026
- DeepFake Face Detection using Machine Learning · International Research Journal of Modernization in Engineering Technology & Science · 2026
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