Sign language is the primary means of communication for many people who are deaf or hard of hearing. The advancement of automatic sign language recognition systems is essential to enhance accessibility and reduce communication barriers. However, the lack of robust and accessible technological solutions still poses a significant obstacle to the full social inclusion of this population. In this context, video signal recognition systems have gained prominence, especially with the advancement of computer vision techniques based on deep learning. This study presents a multimodal recognition approach that combines RGB and depth video data through an attention-based fusion mechanism. The proposed architecture employs two pre-trained Multiscale Vision Transformers (MViT) to extract spatiotemporal features from each modality. These features are then integrated using an attention module that dynamically adjusts the contribution of each input stream. Experiments were conducted using the LIBRAS-UFOP dataset, which contains 56 signs performed by five different individuals, grouped into four linguistic categories. To evaluate the model’s performance, two evaluation protocols were employed: a random data split and a leave-one-signer-out strategy to assess the model’s ability to generalize to unseen users. The results show that while multimodal fusion provided modest improvements in the random split scenario, it achieved significantly higher accuracy when evaluated on unseen signers. These findings demonstrate the value of combining multiple modalities and leveraging attention mechanisms to address user variability, ultimately contributing to more robust and reliable sign language recognition in real-world applications.

Skin cancer is one of the cancers that most aggravates the problem in public health. Among the types of cancer, melanoma is the most aggressive type. Its early diagnosis is essential to increase the possibility of adequate treatment, aiming to reduce the mortality rate. Dermatologists generally use manual methods to diagnose skin lesions. These methods, in addition to being time-consuming, as they are performed manually, can present different results for the same lesion when analyzed by different specialists. Therefore, an automated diagnosis may be necessary to deal with this issue as well as avoid invasive tests. For this, the task of segmenting the skin lesion in the dermoscopic image can be fundamental, as it is a basic task in the image analysis process. In the present work, a Convolutional Neural Network (CNN) model, based on the U-Net, is used to segment the lesion in dermoscopic images. This proposal achieved an accuracy of 0.949 and Jaccard of 0.833 for the 2017 ISIC base, and an accuracy of 0.954 and Jaccard of 0.850 for the 2018 ISIC base. The proposed model has a simpler architecture, in addition to requiring less computational resources. The experiments made it possible to observe that the proposed model results are promising compared with other CNN models presented in the literature.

Reducing the students' dropout is one of the biggest challenges faced by educational institutions, especially in underdeveloped countries. Identification of the student with the highest risk of dropping out is generally used to apply corrective actions (WHO). Therefore, it is also important to determine WHEN a student will drop out, which is fundamental to planning preventive actions. In this work, we perform a study to quantitatively compare several approaches to address the early identification of dropout students in universities. We categorize our study into three main methods families, i.e., analytical methods, traditional classification methods, and probabilistic methods. The first is exploited at preprocessing step for selecting significant variables into the dropout identification task. The second uses machine learning models to classify students into dropout prone or non-dropout prone classes. The third family uses survival models to determine when the student would desert. To evaluate the predictive capacity of the classification models, the Kappa coefficient was incorporated into the usual machine learning metrics and shows that Kappa is handy for evaluating performance in unbalanced data. Similarly, in the survival models, the concordance index was applied to evaluate the predictive capacity. Our approach was applied over a real data set of Peruvian university graduate students to identify when and who will drop out.

Ear recognition has gained attention in recent years. The possibility of being captured from a distance, contactless, without the cooperation of the subject and not be affected by facial expressions makes ear recognition a captivating choice for surveillance and security applications, and even more in the current COVID-19 pandemic context where modalities like face recognition fail due to mouth and facial covering masks usage. Applying any deep learning (DL) algorithm usually demands a large amount of training data and appropriate network architectures, therefore we introduce a large-scale database and explore fine-tuning pre-trained convolutional neural networks (CNNs) looking for a robust representation of ear images taken under uncontrolled conditions. Taking advantage of the face recognition field, we built an ear dataset based on the VGGFace dataset and use the Mask-RCNN for ear detection. Besides, adapting the VGGFace model to the ear domain leads to a better performance than using a model trained for general image recognition. Experiments on the UERC dataset have shown that fine-tuning from a face recognition model and using a larger dataset leads to a significant improvement of around 9% compared to state-of-the-art methods on the ear recognition field. In addition, we have explored score-level fusion by combining matching scores of the fine-tuning models which leads to an improvement of around 4% more. Open-set and close-set experiments have been performed and evaluated using Rank-1 and Rank-5 recognition rate metrics.

The problem of violence detection consists of identifying scenes that characterize violence in a video stream. The violent actions in question can be of the most diverse, from fights, pushes, and robberies to shots and explosions. Detecting the presence of violence is useful for classifying videos and films, blocking inappropriate content for specific audiences, and improving security personnel's performance responsible for areas under surveillance. This work proposes an approach based on the Dynamic Images method, using handcrafted and CNN features the Bag of Visual Words paradigm and a SVM classifier to detect violent actions that involve corporal struggle in the video streams of databases of literature. The proposed methods can achieve an average accuracy of 97.50% for the Hockey dataset, 99.80% for the Movies dataset, and 93.40% for the Crowd dataset. Besides, the identification of violence in each video was performed in of hundredths of a second. Also, the techniques proposed in this work have the advantage that they can be applied even in environments where computational resources are limited, and technologies such as GPU or parallel processing are not available.