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Facial expression recognition (FER) is an emerging and multifaceted research topic. Applications of FER in healthcare, security, safe driving, and so forth have contributed to the credibility of these methods and their adoption in human-computer interaction for intelligent outcomes. Computational FER mimics human facial expression coding skills and conveys important cues that complement speech to assist listeners. Similarly, FER methods based on deep learning and artificial intelligence (AI) techniques have been developed with edge modules to ensure efficiency and real-time processing. To this end, numerous studies have explored different aspects of FER. Surveys of FER have focused on the literature on hand-crafted techniques, with a focus on general methods for local servers but largely neglecting edge vision-inspired deep learning and AI-based FER technologies. To consider these missing aspects, in this study, the existing literature on FER is thoroughly analyzed and surveyed, and the working flow of FER methods, their integral and intermediate steps, and pattern structures are highlighted. Further, the limitations in existing FER surveys are discussed. Next, FER datasets are investigated in depth, and the associated challenges and problems are discussed. In contrast to existing surveys, FER methods are considered for edge vision (on e.g., smartphone or Raspberry Pi, devices, etc.), and different measures to evaluate the performance of FER methods are comprehensively discussed. Finally, recommendations and some avenues for future research are suggested to facilitate further development and implementation of FER technologies.

Hyperspectral images (HSIs) contain subtle spectral details and rich spatial contextures of land cover that benefit from developments in spectral imaging and space technology. The classification of HSIs, which aims to allocate an optimal label for each pixel, has broad prospects in the field of remote sensing. However, due to the redundancy between bands and complex spatial structures, the effectiveness of the shallow spectral–spatial features extracted by traditional machine-learning-based methods tends to be unsatisfying. Over recent decades, various methods based on deep learning in the field of computer vision have been proposed to allow for the discrimination of spectral–spatial representations for classification. In this article, the crucial factors to discriminate spectral–spatial features are systematically summarized from the perspectives of feature extraction and feature optimization. For feature extraction, techniques to ensure the discrimination of spectral features, spatial features, and spectral–spatial features are illustrated based on the characteristics of hyperspectral data and the architecture of models. For feature optimization, techniques to adjust the feature distances between classes in the classification space are introduced in detail. Finally, the characteristics and limitations of these techniques and future challenges in facilitating the discrimination of features for HSI classification are also discussed further.

We investigate the impact of different data modalities for cattle weight estimation. For this purpose, we collect and present our own cattle dataset representing the data modalities: RGB, depth, combined RGB and depth, segmentation, and combined segmentation and depth information. We explore a recent vision-transformer-based zero-shot model proposed by Meta AI Research for producing the segmentation data modality and for extracting the cattle-only region from the images. For experimental analysis, we consider three baseline deep learning models. The objective is to assess how the integration of diverse data sources influences the accuracy and robustness of the deep learning models considering four different performance metrics: mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2). We explore the synergies and challenges associated with each modality and their combined use in enhancing the precision of cattle weight prediction. Through comprehensive experimentation and evaluation, we aim to provide insights into the effectiveness of different data modalities in improving the performance of established deep learning models, facilitating informed decision-making for precision livestock management systems.