This research investigates photoplethysmography (PPG) signals as a reliable, non-invasive method for biometric recognition, addressing modern security needs with a unique and hard-to-replicate approach. A dedicated acquisition system was designed to collect high-quality PPG data from 40 individuals under a strict protocol, ensuring data consistency and accuracy. Advanced filtration minimised noise, while preprocessing steps - including data augmentation and normalisation - enhanced dataset diversity, optimising model training. Five deep learning models were evaluated: a 1D convolutional neural network (1D CNN), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), gated recurrent unit (GRU) and dense neural network (DNN). The 1D CNN performed exceptionally well, achieving a classification accuracy of 99.94%, making it ideal for real-time identification applications. These findings underscore the promise of PPG signals for biometric systems and demonstrate the powerful advantages of deep learning, particularly the 1D CNN, in delivering high identification accuracy across varied environments and practical applications.
In order to solve the problems of high accuracy and recall, as well as high cross entropy loss in traditional methods, a recognition method of multimodal emotion based on graph convolutional neural network is proposed. Utilise independent component analysis algorithm to achieve signal pre-processing, calculate differential entropy features of EEG and ECG signals, and screen key signal features. Using graph convolutional neural networks to extract frequency domain features of signals, incorporating attention mechanisms into bidirectional long short-term memory networks to extract time domain features, and fusing the extracted time domain and frequency domain features to form a comprehensive feature vector. Using Dempster Shafer evidence theory based on feature vectors to determine classifier results and achieve rapid recognition of multimodal emotions. Experimental results have shown that the accuracy and recall of our method consistently remain above 93%, with a cross entropy loss of only 0.05.
A Chinese speech emotion recognition method based on improved convolutional neural network is proposed with the expected goal of solving the problems of high false acceptance rate and false rejection rate and high recognition time consumption in traditional Chinese speech emotion recognition methods. Collect Chinese speech signals and perform pre emphasis, framing, windowing, and fast Fourier transform on the collected signals to achieve pre-processing of Chinese speech signals and extract features of the pre-processed Chinese speech signals. Introducing multi-level residuals to improve the convolutional neural network, inputting Chinese speech signal features into the improved convolutional neural network, and iteratively outputting Chinese speech emotion recognition results. Through experimental testing, it has been proven that the proposed method has an average false acceptance rate of 2.84% and an average false rejection rate of 4.63%. The maximum time consumption for Chinese speech emotion recognition is 49.2 ms.
In order to solve the problems of low accuracy and poor response speed in smartphone face recognition unlocking, a smartphone unlocking face recognition method based on the fusion of global and local features is proposed. Firstly, perform bilinear interpolation and greyscale processing on the facial image. Secondly, the LBP algorithm and ASM model are used to extract local and global features of facial images. Again, integrate local and global features. Finally, based on SVM, the optimal hyperplane for classifying facial image sample data is constructed, and a kernel function is introduced to unlock facial recognition for smartphones. Through experiments, it has been shown that the unlocking recognition error of the proposed method is always below 0.2%, the equilibrium point in the P-R curve is at 0.83, and the average response time of unlocking recognition remains below 0.5s. The experimental data is superior to the comparative method, and the accuracy of face recognition is high, the response speed is fast, and it has good application effects.
