Feature detection and description are fundamental tasks in computer vision and image processing. In this paper, we have performed experiments using six existing Feature detection and description techniques namely, Scale-Invariant Feature Transform (SIFT), Binary Robust Independent Elementary Features (BRIEF), Speeded-Up Robust Features (SURF) and its variant for Biometric CryptoSystems namely, BRIEF based fuzzy vault scheme in Biometric CryptoSystem (BRIEFBCS), SURF based fuzzy vault scheme in Biometric CryptoSystem (SURFBCS) and SIFT based fuzzy vault scheme in Biometric CryptoSystem (SIFTBCS) on MMU and YALE datasets. We present a comprehensive comparative analysis of these widely used methods. These methods are evaluated on the basis of various parameters such as: feature extraction time, feature description time, overall execution time, security component, rotation invariance, scale invariance, illumination invariance, descriptor type, key construction time, robustness, etc. Through a systematic evaluation, we aim to provide insights into the strengths and weaknesses of each method, aiding practitioners in choosing the most suitable approach for their specific applications.