A Hybrid Model for Biometric Authentication using Finger Back Knuckle Surface based on Angular Geometric Analysis

Biometric based personal recognition is an efficient method for identifying a person. Recently, hand based biometric has become popular due to its various advantages such as high verificat ion accuracy and high user acceptability. This paper proposes a hybrid model using an emerging hand based biometric trait known as Finger Back Knuckle Surface. This model is based on angular geometric analysis which is implemented on two different samples of Finger Back Knuckle Surface such as Finger Bend Knuckle Surface and Finger Intact Knuckle Surface for the ext raction of knuckle feature informat ion. The obtained feature informat ion from both the surfaces is fused using feature information level fusion technique to authenticate the individuals. Experiments were conducted using newly created database for both Bend Knuckle and Intact Knuckle Surface. The results were promising in terms of accuracy, speed and computational complexity. Index Terms — Finger Back Knuckle Surface, Finger Bend Knuckle Surface, Finger Intact Knuckle Surface, Angular Geometric analysis, Tangents and Secants Method, Feature Informat ion level fusion, Correlation Coefficient