Face Recognition: Latest Trends and Future Perspectives
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".
Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 5163
Special Issue Editor
Special Issue Information
Dear Colleagues,
Face recognition is the most prominent biometric technique for identity authentication and is widely used in applications such as access control, time attendance, finance, law enforcement, public security, forensics, and human–computer interactions. It is a long-standing research topic, with over 50 years of research and development. Advances in deep learning and neural networks have significantly reshaped the landscape of face recognition research and applications in almost all aspects. The key engine of recent face recognition consists of network architecture evolution, a variety of loss functions, and growing face benchmarks. Even though 2D face recognition approaches reached some degree of maturity and reported very high rates of recognition across different benchmarks, the recognition performance degrades dramatically when there are large appearance variations (e.g., ageing, pose, resolution and expression) and poor environmental conditions (e.g., lighting and occlusion). In this Special Issue, we will present the history of face recognition technology, the current state-of-the-art methodologies, and future challenges existing in uncontrolled face recognition. We will specifically concentrate on the most recent large-scale databases, deep learning approaches and sophisticated loss designs in this field. Open issues will be examined and potential directions for research in facial recognition will be proposed to provide the reader with a point of reference for topics that deserve further consideration and exploration.
Dr. Jiankang Deng
Guest Editor
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Keywords
- uncontrolled face recognition
- deep face recognition
- age-invariant face recognition
- pose-invariant face recognition
- occlusion-robust face recognition
- large-scale face benchmarks
- deep face recognition
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