Multi-algorithmic Fingerprint authentication using minutia descriptor and average SIFT keypoint descriptors is a comprehensive approach for fingerprint authentication that integrates minutia descriptor and SIFT keypoint descriptors for representing fingerprint information. In this system, multiple algorithms work collaboratively on fingerprint images, each designed to gather information on an individual’s physiological biometric traits. As a first step, preprocessing is done on fingerprint images to eliminate noise. The features are extracted from these preprocessed images. The proposed algorithm will combine two feature extraction algorithms to create a feature that integrates a fingerprint image’s texture information and minutiae points. Features extracted from these various algorithms are fused to generate a unified and more reliable decision regarding the person’s identity. Here, we create a new template by combining features such as the minutia point descriptor and the average of SIFT keypoint descriptors. We performed tests on FVC 2002 DB1 and DB2 databases and obtained an EER of 1.2% and 1.4%, respectively, showing the effectiveness of the proposed multi-algorithmic fusion of two descriptors.