Automatic speaker identification and verification in diverse acoustic environments remains a major challenge due to the high variability of speech signals, particularly due to emotional variations and ambient noise. This paper presents a novel approach to improve the robustness and accuracy of speaker recognition systems in these complex conditions. Our method combines advanced preprocessing of audio signals with a hybrid neural network architecture, designed to efficiently capture the spatiotemporal characteristics of speech spectrograms. The proposed preprocessing process transforms audio signals of varying durations into uniform spectral representations, facilitating their analysis by deep learning models. The developed neural architecture integrates Temporal Convolutional Layers (TCN) and Long Short-Term Memory (LSTM) recurrent networks, enriched by an additive attention mechanism exploiting auxiliary information on gender and emotions. Experiments conducted on the RAVDESS and CREMA-D databases demonstrate the remarkable effectiveness of our approach. For speaker identification, the model achieves an accuracy higher than 96% on both bases. For speaker authentication, it displays a global Equal Error Rates (EER) of 1.75% for RAVDESS and 1.60% for CREMA-D, significantly outperforming some existing methods.

Online signature recognition is a prominent biometric system reliable for users’ verification that leverages the dynamic aspects of signatures. This paper presents a novel approach for dynamic signature verification and identification by combining temporal and spectral analysis techniques. The proposed method extracts discriminative features from both spatial coordinates and their derivatives, such as velocity, acceleration, and pen speed. It also applies data augmentation techniques such as rotation, scaling, and temporal distortion, to generate static images of the signatures and use spectrograms for further analysis. The experiments carried out on the SVC2004 Task 2 database, which includes genuine and skilled forgery signatures, have shown the effectiveness of the proposed method. Indeed, for verification mode, the use of Artificial neural network allows achieving a lower EER of up 2.11%, highlighting its potential for reliable signature verification. Furthermore, exploring signature images with Convolutional neural network for identification mode, allows achieving an accuracy of 99.56%. This study highlights the effectiveness of combining temporal and spectral features for signature identification and verification in security-sensitive applications.

Palm vein recognition is a biometric method for individuals’ authentication and/or identification based on the unique patterns of veins in the palms of their hands. This paper presents a comprehensive study of the Convolutional Neural Network (CNN) for palm vein recognition. Several CNN architectures, such as VGGNet, AlexNet, and ZFNet, have been studied and adapted by proposing an improved version based on the optimization of their parameters. Two hybrid approaches based on the fusion of the improved versions of these CNN models are proposed. The proposed method was evaluated on near-infrared palm vein images from MS-PolyU database using data augmentation. The experimental results carried out have shown the high accuracy of the proposed method, allowing achieving an accuracy rate of up to 99.72%.