Identity-recognition technologies require assistive equipment, whereas they are poor in recognition accuracy and expensive. To overcome this deficiency, this paper proposes several gait feature identification algorithms. First, in combination with the collected gait information of individuals from triaxial accelerometers on smartphones, the collected information is preprocessed, and multimodal fusion is used with the existing standard datasets to yield a multimodal synthetic dataset; then, with the multimodal characteristics of the collected biological gait information, a Convolutional Neural Network based Gait Recognition (CNN-GR) model and the related scheme for the multimodal features are developed; at last, regarding the proposed CNN-GR model and scheme, a unimodal gait feature identity single-gait feature identification algorithm and a multimodal gait feature fusion identity multimodal gait information algorithm are proposed. Experimental results show that the proposed algorithms perform well in recognition accuracy, the confusion matrix, and the kappa statistic, and they have better recognition scores and robustness than the compared algorithms; thus, the proposed algorithm has prominent promise in practice.
In this paper, we present a new iterative blind multispectral image restoration algorithm based on double regularization (DR). The motivation for DR when applied to multispectral restoration lies in its effectiveness towards edge preservation in joint blur identification and image restoration. With consideration for both the intra-and inter-channel blurring function in the multiple-input multiple-output (MIMO) systems, an alternating minimization (AM) procedure with conjugate gradient optimization (CGO) scheme is formulated to implement restoration iteratively. The derivation of DR optimization shows that optimal restoration result can be achieved even when the MIMO systems suffer from interchannel interference. Experimental results show that it is effective in performing blind mutichannel restoration when applied to color images.
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