4 research outputs found
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Masked face recognition using deep learning: a review
A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR
Cultivating historical heritage area vitality using urban morphology approach based on big data and machine learning
The conservation of historical heritage can bring social benefits to cities by promoting community economic development and societal creativity. In the early stages of historical heritage conservation, the focus was on the museum-style concept for individual structures. At present, heritage area vitality is often adopted as a general conservation method to increase the vibrancy of such areas. However, it remains unclear whether urban morphological elements suitable for urban areas can be applied to heritage areas. This study uses ridge regression and LightGBM with multi-source big geospatial data to explore whether urban morphological elements that affect the vitality of heritage and urban areas are consistent or have different spatial distributions and daily variations. From a sample of 12 Chinese cities, our analysis shows the following results. First, factors affecting urban vitality differ from those influencing heritage areas. Second, factors influencing urban and heritage areas' vitality have diurnal variations and differ across cities. The overarching contribution of this study is to propose a quantitative and replicable framework for heritage adaptation, combining urban morphology and vitality measures derived from big geospatial data. This study also extends the understanding of forms of heritage areas and provides theoretical support for heritage conservation, urban construction, and economic development