Palmprint verification using gradient maps and support vector machines

With the urgent demand in information security, biometric feature-based verification systems have been extensively explored in many application domains. However, the efficacy of existing biometric-based systems is unsatisfactory and there are still a lot of difficult problems to be solved. Among many existing biometric features, palmprint has been regarded as a unique and useful biometric feature due to its stable principal lines. In this paper, we proposed a new method to perform palmprint recognition. We extract the gradient map of a palmprint and then verify it by a trained support vector machine (SVM). The procedure can be divided into three steps, including image preprocessing, feature extraction, and verification. We used the multi-spectral palmprint database prepared by Hong Kong PolyU [14] which included 6000 palm images collected from 250 individuals to test our method. The experimental results demonstrate our proposed method is reliable and efficient to verify whether the person is genuine or not.