Features such as face, fingerprint, and iris imprints have been used for authentication in
biometric system. The toughest feature amongst these is the face. Extracting a region with
the most potential face features from an image for biometric identification followed by illumination enhancement is a commonly used method. However, the region of interest extraction followed by illumination enhancement is sensitive to image face feature displacement, skewed image, and bad illumination. This research presents a cancell able face image algorithm built upon the speeded-up robust features method to extract and select features. A speeded-up robust feature approach is utilised for the image’s features extraction, while Winner-Takes-All hashing is utilised for match-seeking. Finally, the features vectors are projected by utilising a random form of binary orthogonal matrice. Experiments were conducted on Yale and ORL datasets which provide gray scale images of sizes 168 × 192 and 112 × 92 pixels, respectively. The execution of the proposed
algorithm was measured against several algorithms using equal error rate metric. It is found that the proposed algorithm produced an acceptable performance which indicates that this algorithm can be used in biometric security applications
