Design Simulation of Predicting Age and Gender for Human using Machine Learning Approach
Keywords:PSNR, IOT, Encryption, SVM, Image, Emotion, Image Processing, EBIR
The amount of audiovisual information that is now readily available in digital format has grown significantly in recent years. Terabytes of brand-new images, audio files, and video clips are produced and preserved every day, resulting in a vast, dispersed, and generally unstructured library of multimedia material that is primarily accessible via the Internet. Multimedia data may now be digitalized, compressed, and archived easily, cheaply, and with a variety of hardware and software supporting these processes. The subsequent recovery of the stored knowledge, however, could need a large amount of extra labor in order to be effective and successful. Searching the media for clues about the human emotions depicted in the photos, audio, or video snippets is known as emotion-based retrieval. This kind of intricate system takes user input and processes it to produce useful results. They can take into account the color of the image, the objects in it, its categorization (such as outdoors or inside), and its emotion (also called mood or feeling). Depending on how it is read, the final one can refer to either the emotional content of an image or the effect it has on a person. The proposed study emphasizes the use of SVMs for picture segmentation, emotion analysis, and age, gender, and mood identification via feature extraction and classification. The proposed study emphasizes the use of SVMs for picture segmentation, emotion analysis, and age, gender, and mood identification via feature extraction and classification.