Combining Gabor Local Texture Pattern and Wavelet Global Features for Fingerprint Matching

Fingerprint verification is one of the most well-known and publicized biometrics. Fingerprints have been matched over the decades using minutiae features. However, minutiae extraction from poor-quality or incomplete images is not reliable and often gives erroneous results in matching. Further, due to variations caused by the users, minutiae alignment and matching may not be exact. Fingerprints exhibit oriented texture-like patterns. Gabor filters can optimally capture local frequency and orientation information even from poor-quality or incomplete images. Like-wise, global texture information can be efficiently extracted using wavelet transform. This paper proposes a hybrid fingerprint verification system based on local texture pattern obtained using Gabor filtering and wavelet global features obtained by multiresolution analysis of a fingerprint. Experimental results show that the proposed hybrid system is efficient and suitable for real-time authentication applications with a small size database.