This study explores an adaptive sparse representation approach for online writer identification. The main focus is on employing prior information that quantifies the degree of importance of a dictionary atom concerning a given writer. This information is proposed by a fusion of two derived components. The first component is a saliency measure obtained from the sum-pooled sparse coefficients corresponding to the sub-strokes of a set of enrolled writers. The second component is a similarity score, computed for each dictionary atom with regards to a given writer, that is related to the reconstruction error of the sub-stroke based feature vectors. The proposed identification is accomplished with an ensemble of support vector machines (SVMs), wherein the input to the SVM trained for a writer is obtained by incorporating the adapted saliency values of that writer on the document descriptor obtained via average pooling of sparse codes. Experiments performed on the IAM and IBM-UB1 online handwriting databases demonstrate the efficacy of the proposed scheme.

中文翻译：

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基于自适应稀疏表示框架的在线作者识别系统

本研究探索了一种用于在线作者识别的自适应稀疏表示方法。主要重点是采用先验信息，该信息量化与给定作者有关的字典原子的重要程度。通过两个派生组件的融合来提出此信息。第一部分是从与一组已注册作者的子笔划相对应的总和稀疏系数中获得的显着性度量。第二部分是针对给定作者针对每个字典原子计算的相似性分数，该分数与基于子笔划的特征向量的重构误差有关。建议的识别是通过一组支持向量机（SVM）完成的，其中，通过将写作者的调整后的显着性值合并到通过稀疏代码的平均池获得的文档描述符上，来获得为写作者训练的SVM的输入。在IAM和IBM-UB1在线手写数据库上进行的实验证明了该方案的有效性。