Parkinson’s disease (PD) is a chronic and progressive neurodegenerative disorder that severely impacts motor functions, including handwriting. Early and precise detection is essential for timely intervention and effective treatment. In this paper, we propose a novel hybrid approach that integrates handcrafted and deep learning-based (DL) features extracted from handwriting samples. By leveraging both feature types, our method provides a more comprehensive analysis, enhancing the accuracy and robustness of PD detection. Indeed, Handcrafted features provide explicit, interpretable descriptors that capture domain-specific patterns while DL features encode high-level abstract representations. We first extracted DL features using a fine-tuned transfer learning model based on ResNet50 and combined them with a set of handcrafted features. Similarly, DL features were obtained using a second fine-tuned transfer learning model based on MobileNetV2, and the derived features were integrated with the same handcrafted feature set for further analysis. Afterward, a feature selection step is performed.The conducted experiments exploiting a voting classifier, demonstrates that the proposed hybrid approach achieve promising results reaching an accuracy of 91.51%.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that leads to motor impairments. Early detection is crucial for effective treatment; however, conventional diagnostic methods are often costly, time-consuming, and inaccessible, limiting their widespread clinical adoption. To address these challenges, we propose a novel hybrid approach that merges Deep Learning (DL) features extrated from handwriting images with voice characteristics using an optimized machine learning (ML) classification technique. The integration of multimodal data enhances robustness by reducing dependency on a single biomarker, making PD diagnosis more reliable. We begin by augmenting both of the datasets size to expand samples diversity. Then, we combine DenseNet201’s detailed features with ResNet50’s robust spatial features to enhance analytical precision and capture both fine-grained and high-level patterns of handwriting images. The obtained DL features are fused with voice characteristics leveraging complementary information. Afterwards, feature selection is performed using Fisher’s score to retain the most relevant attributes, further boosting classification accuracy. We achieve an accuracy of 92.31%, demonstrating superior performance compared to state-of-the-art methods.

3D data are an important resource for many computer-based applications, as they provide valuable depth cues about the full geometry of 3D associated objects. They become even more valuable as regards 3D face/ facial expression recognition using deep learning. Indeed, two main challenges remain under study. The first is how to resume 3D faces with a discriminative representation from a 3D point cloud while exploiting an adequate Deep Neural Network (DNN). The second is the lack of large 3D facial datasets. To address the first issue, we propose to exploit solely geometric information while applying DNN. Hence, in order to deal with high resolution face scans with a rich point cloud representation, we extract point-based representations using various sampling strategies. Different keypoint sets are used, ranging from a small set of points of interest (i.e. landmarks) to point sets sampled from a curve-based representation, as well as scale-invariant feature transform keypoints. As for the second issue and in order to overcome overfitting caused mainly by the lack of large labelled datasets while applying DNN, we propose to generate new realistic-like facial expressions using non-rigid registration techniques. The effectiveness of the suggested approach is demonstrated through conducting experiments on the BU-3DFE database. The quantitative evaluation and comparison with the recently developed state of the art show the competitiveness of the proposed 3D facial expression recognition approach.

In this paper we propose a new approach for watermarking biometric fingerprint images using Gabor direct linear discriminant analysis face features. Our goal is to incorporate a watermark in the best embedding domain that preserves minutiae, which are the most relevant proven features of a fingerprint. We conducted a comprehensive study based on the influence of the watermark embedding domain choice on the performance of the minutiae-based identity and of the robustness and imperceptibility of the watermarking approach. Three embedding domains were tested: spatial, frequency and multiresolution. The various tests were performed on two biometric databases, multimodal and chimerical. The best results were recorded in the multiresolution domain in terms of preserving the minutiae number and positions. Moreover, this embedding domain led to the best verification performances and to a good compromise between robustness and imperceptibility.