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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In order to improve the accuracy of brain signal processing and accelerate speed meanwhile, we present an optimal and intelligent method for large dataset classification application in this paper. Optimized Extreme Learning Machine (OELM) is introduced in ElectroCorticoGram (ECoG) feature classification of motor imaginary-based brain-computer interface (BCI) system, with common spatial pattern (CSP) to extract the feature. When comparing it with other conventional classification methods like SVM and ELM, we exploit several metrics to evaluate the performance of all the adopted methods objectively. The accuracy of the proposed BCI system approaches approximately 92.31% when classifying ECoG epochs into left pinky or tongue movement, while the highest accuracy obtained by other methods is no more than 81%, which substantiates that OELM is more efficient than SVM, ELM, etc. Moreover, the simulation results also demonstrate that OELM will significantly improve the performance with p value being far less than 0.001. Hence, the proposed OELM is satisfactory in addressing ECoG signal.

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Brain-computer interface (BCI) systems establish a direct communication channel from the brain to an output device. As the basis of BCIs, recognizing motor imagery activities poses a considerable challenge to signal processing due to the complex and non-stationary characteristics. This paper introduces an optimal and intelligent method for motor imagery BCIs. Because of the robustness to noise, wavelet packet decomposition and common spatial pattern (CSP) methods were implemented to reduce the dimensions of preprocessed signals. And a novel and efficient classifier projection extreme learning machine (PELM) was employed to recognize the labels of electroencephalogram signals. Experiments have been performed on the BCI Competition Dataset to demonstrate the superiority of wavelet-CSP in BCI and the outperformance of the PELM-based method. Results show that the average recognition rate of PELM approaches approximately 70%, while the optimal rate of other methods is 72%, whose training time and classification time are relatively longer as 11.00 ms and 11.66 ms, respectively, compared with 4.75 ms and 4.87 ms obtained by using the proposed BCI system.