Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied.
In this paper, we present a parallel framework based on MPI for a large dataset to extract power spectrum features of EEG signals so as to improve the speed of brain signal processing. At present, the Welch method has been wildly used to estimate the power spectrum. However, the traditional Welch method takes a lot of time especially for the large dataset. In view of this, we added the MPI into the traditional Welch method and developed it into a reusable master-slave parallel framework. As long as the EEG data of any format are converted into the text file of a specified format, the power spectrum features can be extracted quickly by this parallel framework. In the proposed parallel framework, the EEG signals recorded by a channel are divided into N overlapping data segments. Then, the PSD of N segments are computed by some nodes in parallel. The results are collected and summarized by the master node. The final PSD results of each channel are saved in the text file, which can be read and analyzed by Microsoft Excel. This framework can be implemented not only on the clusters but also on the desktop computer. In the experiment, we deploy this framework on a desktop computer with a 4-core Intel CPU. It took only a few minutes to extract the power spectrum features from the 2.85 GB EEG dataset, seven times faster than using Python. This framework makes it easy for users, who do not have any parallel programming experience in constructing the parallel algorithms to extract the EEG power spectrum.
Multimodal biometric authentication method can conquer the defects of the unimodal biometric authentication technology. In this paper, we design and develop an efficient Android-based multimodal biometric authentication system with face and voice. Considering the hardware performance restriction of the smart terminal, including the random access memory (RAM), central processing unit (CPU) and graphics processor unit (GPU), etc., which cannot efficiently accomplish the tasks of storing and quickly processing the large amount of data, a face detection method is introduced to efficiently discard the redundant background of the image and reduce the unnecessary information. Furthermore, an improved local binary pattern (LBP) coding method is presented to improve the robustness of the extracted face feature. We also improve the conventional endpoint detection technology, i.e. the voice activity detection (VAD) method, which can efficiently increase the detection accuracy of the voice mute and transition information and boost the voice matching effectiveness. To boost the authentication accuracy and effectiveness, we present an adaptive fusion strategy which organically integrates the merits of the face and voice biometrics simultaneously. The cross-validation experiments with public databases demonstrate encouraging authentication performances compared with some state-of-the-art methods. Extensive testing experiments on Android-based smart terminal show that the developed multimodal biometric authentication system achieves perfect authentication effect and can efficiently content the practical requirements.
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