Facial emotion recognition (FER) is a significant subject in computer vision and artificial intelligence because of its tremendous academic and commercial potential, such as in cognitive science, health care, virtual reality, and video conferencing in various domains. While FER could be carried out using multiple sensors, this review includes research that exclusively uses face images, since facial expressions are among the key pieces of knowledge in human interactions. We give a brief review of FER research carried out in recent years. We divided the techniques of FER mainly into two parts, i.e., based on the type of approach and based on the type of frame, which is further subdivided into two sub-parts of each classification for more detailed division. First, it explains traditional FER approaches together with a description of the representative classes of FER systems. Deep-learning FER strategies are then addressed using deep networks that allow “end-to-end” learning. This review is also directed toward an up-to-date deep learning strategy, which is a trending topic nowadays. A brief overview of publicly accessible assessment metrics is provided in the later part of this paper, as well as a comparison with the baseline results is presented, which is a norm for quantitative analysis of FER research. The whole analysis also could act as a concise field guide for beginners in the FER sector, providing general details and a common understanding of both the recent state-of-the-art research and established researchers searching for fruitful areas for further research.
Underwater robotics is an important part of oceanography, resource exploration, and marine engineering. The images captured by underwater robots are affected by environmental factors, such as scattering and absorption, which lead to color cast and haze issues. We propose a light-weight fusion-based convolution neural network (FCNN) that improves the visual content of the underwater image. In FCNN, two methods, such as automatic white balancing and contrast limited adaptive histogram equalization, are used in the preprocessing phase. Then the outputs of the preprocessing phase are fed into the multilayer neural network to learn the end-to-end features. Finally, the outputs of the preprocessing phase and multilayer neural network are fused to obtain enhanced image. The performance of FCNN is evaluated based on qualitative, quantitative analysis, and in terms of time complexity. The experimental analysis proves that the FCNN overcomes the existing conventional methods and deep learning-based networks.
Underwater environments can be used to explore new resources that can be employed in the fields of medical science and energy resources. Humans are dependent on the valuable resources that exist beneath Earth’s surface. Underwater exploration requires enhanced images that are obtained using enhancement methods. So, it is important that underwater image enhancement (UIE) methods work well in terms of performance and accuracy. As a result, research in UIE has increased in the past few years. An extensive survey is conducted on existing UIE methods along with their broad classification, underwater datasets, and evaluation metrics, respectively. The experimental analysis is conducted to compare the existing UIE methods in terms of qualitative and quantitative evaluation metrics. The real-world applications and future scope of existing enhancement methods are highlighted and discussed.
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