Feature detection and matching are an essential component of many computer vision applications. It is used for variety of applications for example aligning the two images so that they can be seamlessly stitched into a composite mosaics or establishing a dense set of correspondences so that a 3D model can be constructed. But selecting the features to compare and match the images is a still ongoing search area. There have been many algorithms implemented for this purpose. In this assignment we try to implement and review some algorithms like HARRIS, MSER, SIFT, PCA-SIFT, GLOH and then we test our algorithms in the symfeat dataset that contains image pairs exhibiting a range of dramatic variations in lighting, age, and rendering style.