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Robust face recognition in real-world surveillance videos is a challenging but important issue due to the needs of practical applications such as security monitoring. While current face recognition systems perform well in relatively constrained scenes, they tend to suffer from variations in pose, illumination or facial expression in real-world surveillance videos. In this paper, we propose a method...
Class participation enrollment, an effective means of classroom management, can urge students to attend class on time and ensure the quality of teaching. Almost all schools are using a roll-call to check and record whether students are coming to class or not. The time required for this method depends on the number of students, which is a huge waste of time. In recent years, with the development of...
Face images captured by real-world video surveillance applications usually have low resolution. This leads to poor performance or even failure of most face recognition algorithms. As a consequence, identifying the face of the query in low resolution, based on the high-resolution image gallery, proves to be a huge challenge. To address this problem, a novel multi-resolution convolutional neural network...
Most current methods of facial recognition rely on the condition of having multiple samples per person available for feature extraction. In practical applications, however, only one sample may be available for each person to train a model with. As a result, many of the traditional methods fall short, leaving the challenge of facial recognition greater than ever. To deal with this challenge, this study...
A new algorithm is proposed to deal with single training sample face recognition. After geometric normalization of human faces, we generate 13 virtual samples for each face by using geometric transformation and svd decomposition. The distribution of gray value of each image is processed to be normal standard distribution. Finally sparse representation is used to recognize faces. The result of experiments...
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