Gabor wavelets are mathematical functions utilized in signal processing and image analysis, named after physicist Dennis Gabor, who introduced them in the 1940s. They serve to analyze the frequency and time characteristics of signals and are particularly effective for tracking and coding facial expressions. Gabor wavelets are a type of wavelet characterized by a Gaussian envelope, allowing them to capture both low and high-frequency components of a signal. This makes them ideal for applications such as image compression, feature extraction, and pattern recognition. In image analysis, Gabor wavelets are commonly applied through Gabor filtering, which helps extract specific features or textures, aiding in tasks like edge detection, texture classification, and object recognition. Overall, they provide a detailed and accurate means of analyzing complex signals and images.