Facial recognition systems have mostly been employed for gender classification in computer vision over the years. The development of advance tools and concepts in deep learning and computer vision, especially with developing convolutional neural networks t o construct maps of facial features or utilizing transfer learning of models pretrained on huge volume of data, has overtime, further enhanced the credibility and resultant reliability of using facial recognition techniques for many forms of attribute iden tification in computer vision. However, the use of facial recognition for attributes identification and classification are not without pitfalls as most facial recognition techniques require clear images and high - quality videos of clearly defined facial features to be effective [5] . In real life situations, facial images, especially when obtained under non ideal situations, are often distorted, blurred or concentrated on positions that are not appropriate for proper feature detection by facial recognition techniques. For example, co mmon f acial recognition techniques are often unable to detect facial features on images where the face is inside view, taken from distance, partially covered or even blurred. While new innovative techniques, like Google's Mediapipe facial recognition system are addressing some of these limitations, these are still valid problems today for most facial recognition techniques such as the OpenCV Facial Cascades.

The same artificial intelligence method used in facial recognition systems could help improve the prediction of hailstorms and their severity, according to researchers at the National Center for Atmospheric Research. National Center for Atmospheric Research (NCAR) researchers have found that the same artificial intelligence method used in facial recognition systems could help improve the prediction of hailstorms and their severity. The researchers used machine learning to train a convolutional neural network to recognize features of individual storms that affect the formation of hail. The model was trained on images of simulated storms, along with information about temperature, pressure, wind speed, and direction. Once trained, the model was able to determine which features of the storm correlate with whether or not it will hail, and how big the hailstones are likely to be.

The shape of a severe storm, such as this one, is an important factor in whether the storm produces hail and how large the hailstones are, but current hail-prediction techniques are typically not able to take the storm's entire structure into account. NCAR scientists are experimenting with a new machine-learning technique that can process images to weigh the impact of storm shape and potentially improve hail forecasts. This image is freely available for media and nonprofit use.) The same artificial intelligence technique typically used in facial recognition systems could help improve prediction of hailstorms and their severity, according to a new study from the National Center for Atmospheric Research (NCAR). Instead of zeroing in on the features of an individual face, scientists trained a deep learning model called a convolutional neural network to recognize features of individual storms that affect the formation of hail and how large the hailstones will be, both of which are notoriously difficult to predict.