Aiming at the problem that the traditional photoplethysmography (PPG) biometric recognition based on sparse representation is not robust to noise and intraclass variations when the sample size is small, we propose a PPG biometric recognition method based on multifeature deep cascaded sparse representation (MFDCSR). The method consists of multifeature signal coding and deep cascaded coding. The function of multifeature signal coding is to extract the shape, wavelet, and principal component analysis features of the PPG signal and to perform sparse representation. Deep cascaded coding is multilayer feature coding. Each layer combines multifeature signal coding with the result of the previous layer as input, and the output of each layer is the input of the next layer. The function of deep cascade coding is to learn the features of the PPG signal, layer by layer, and to output the category distribution vector of the PPG signal in the last layer. Experiments demonstrate that MFDCSR has better recognition performance than current methods for PPG biometric recognition.

Electrocardiogram (ECG) signal is a promising biometric trait, and many methods have been proposed for ECG biometric recognition. However, it is challenging to design a robust and precise method to improve the recognition performance of ECG signals with noise and signal variation. We present a multi-feature sparse representations learning model via collective matrix factorization for ECG biometric recognition, MSRCMF for short. First, we extract one-dimensional local binary pattern (1D-LBP), shape and wavelet features of ECG signals and then obtain their sparse representations. Second, to extract discriminative information and preserve the intra-and inter-subject similarities, we leverage the collective matrix factorization on multiple sparse representations and the label information to obtain the latent semantic space. At last, we can recognize the ECG signals in the learned semantic space. Extensive experiments on four ECG databases show that MSRCMF can achieve competitive performance compared to state-of-the-art methods.

Due to its importance in many applications

Photoplethysmography (PPG) signal is a novel biometric trait related to the identity of people; many time-and frequency-domain methods for PPG biometric recognition have been proposed. However, the existing domain methods for PPG biometric recognition only consider a single domain or the feature-level fusion of time and frequency domains, without considering the exploration of the fusion correlations of the time and frequency domains. The authors propose a time-frequency fusion for a PPG biometric recognition method with collective matrix factorisation (TFCMF) that leverages collective matrix factorisation to learn a shared latent semantic space by exploring the fusion correlations of the time and frequency domains. In addition, the authors utilise the ℓ 2,1 norm to constrain the reconstruction error and shared matrix, which can alleviate the influence of noise and intra-class variation, and ensure the robustness of learnt semantic space. Experiments demonstrate that TFCMF has better recognition performance than current state-of-the-art methods for PPG biometric recognition.

Due to its importance in machine learning, pattern recognition, and many other applications, uncertain data mining has attracted much attention. This paper proposes a classification method for uncertain data based on a sparse de-noising auto-encoder neural network. Firstly, a hyperellipsoid convex model is used to describe the uncertain interval vector, and give an approach for uncertain data classification based on an interval uncertainty support vector machine. Secondly, this paper introduces a sparse de-noising auto encoder neural network, which can convert highdimension data into low-dimensional characteristic space. Finally, this paper establishes a three layered auto-encoder neural network, and whose deep structure is tuned with stochastic gradient descent parameters fine-tuned by layer greedy pre-training and back propagation. Experimental results show that this proposed method creats better classification accuracy, and has stronger robustness for noise parameter, so it is effective for uncertain data classification.