This work proposes an approach for dynamic facial expression recognition to recognize emotions in controlled environments, due to its computational efficiency. The CREMA-D and RAVDESS datasets are used, from which sequences of 100 frames per video are extracted. Preprocessing for geometric features is performed using Face Mesh and facial alignment, while for deep features, face detection, facial alignment, resizing, and center cropping are applied. Geometric features are computed from the internal angles between facial landmarks, and deep features are extracted using MobileNetV2, ShuffleNetV2, and EfficientNet-B0, followed by dimensionality reduction via NCA. Both representations are concatenated and used as input to an LSTM (for CREMA-D) and a BiLSTM (for RAVDESS). The proposed method achieves UAR/WAR scores of 63.68%/63.71% on CREMA-D and 79.50%/80.21% on RAVDESS, demonstrating that the proposed approach is efficient and competitive without relying on architectures with higher computational cost.

In recent years, the reversible data hiding techniques also known as lossless or invertible data hiding, has gradually become a very active research area. The reversibility of these schemes makes possible to extract the embedded data without errors, as well as to restore the cover medium to its original state. Furthermore, to guarantee the security and confidentiality of the hidden data and the image, reversible data hiding schemes over encrypted domain are presented as a promising solution to solve several issues of information security. This paper presents a study case of reversible data hiding schemes over encrypted domain oriented to the protection of the sharing and distribution of color images hosted in cloud storage services. The experimental results are presented in terms of imperceptibility, capacity, confidentiality, and visual quality, respectively.