Prevalent use of motion capture (MoCap) produces large volumes of data and MoCap data retrieval becomes crucial for efficient data reuse. MoCap clips may not be neatly segmented and labeled, increasing the difficulty of retrieval. In order to effectively retrieve such data, we propose an elastic content-based retrieval scheme via unsupervised posture encoding and strided temporal alignment (PESTA) in this work. It retrieves similarities at the sub-sequence level, achieves robustness against singular frames and enables control of tradeoff between precision and efficiency. It firstly learns a dictionary of encoded postures utilizing unsupervised adversarial autoencoder techniques and, based on which, compactly symbolizes any MoCap sequence. Secondly, it conducts strided temporal alignment to align a query sequence to repository sequences to retrieve the best-matching sub-sequences from the repository. Further, it extends to find matches for multiple sub-queries in a long query at sharply promoted efficiency and minutely sacrificed precision. Outstanding performance of the proposed scheme is well demonstrated by experiments on two public MoCap datasets and one MoCap dataset captured by ourselves.

Electrocardiogram (ECG) biometric recognition has emerged as a hot research topic in the past decade. Although some promising results have been reported, especially using sparse representation learning (SRL) and deep neural network, robust identification for small-scale data is still a challenge. To address this issue, we integrate SRL into a deep cascade model, and propose a multi-scale deep cascade bi-forest (MDCBF) model for ECG biometric recognition. We design the bi-forest based feature generator by fusing L1-norm sparsity and L2-norm collaborative representation to efficiently deal with noise. Then we propose a deep cascade framework, which includes multi-scale signal coding and deep cascade coding. In the former, we design an adaptive weighted pooling operation, which can fully explore the discriminative information of segments with low noise. In deep cascade coding, we propose level-wise class coding without backpropagation to mine more discriminative features. Extensive experiments are conducted on four small-scale ECG databases, and the results demonstrate that the proposed method performs competitively with state-of-the-art methods.