TY - GEN
T1 - Abductive neural network modeling for hand recognition using geometric features
AU - El-Alfy, El Sayed M.
AU - Abdel-Aal, Radwan E.
AU - Baig, Zubair A.
PY - 2012
Y1 - 2012
N2 - Hand recognition has received wide acceptance in many applications for automatic personal identification or verification in low to medium security systems. In this paper, we present a new approach for hand recognition based on abductive machine learning and hand geometric features. This approach is evaluated and compared to other learning algorithms including decision trees, support vector machines, and rule-based classifiers. Unlike other algorithms, the abductive learning approach builds simple polynomial neural network models by automatically selecting the most relevant features for each case. It also has acceptable accuracy with low false acceptance and false rejection rates. For the adopted dataset, the abductive learning approach has more than 98% overall accuracy, 1.67% average false rejection rate, and 0.088% average false acceptance rate.
AB - Hand recognition has received wide acceptance in many applications for automatic personal identification or verification in low to medium security systems. In this paper, we present a new approach for hand recognition based on abductive machine learning and hand geometric features. This approach is evaluated and compared to other learning algorithms including decision trees, support vector machines, and rule-based classifiers. Unlike other algorithms, the abductive learning approach builds simple polynomial neural network models by automatically selecting the most relevant features for each case. It also has acceptable accuracy with low false acceptance and false rejection rates. For the adopted dataset, the abductive learning approach has more than 98% overall accuracy, 1.67% average false rejection rate, and 0.088% average false acceptance rate.
KW - Abductive Learning
KW - Biometric Authentication
KW - Geometric Features
KW - Hand Recognition
KW - Pattern Recognition
KW - Polynomial Neural Networks
UR - https://www.scopus.com/pages/publications/84869073821
U2 - 10.1007/978-3-642-34478-7_72
DO - 10.1007/978-3-642-34478-7_72
M3 - Conference contribution
AN - SCOPUS:84869073821
SN - 9783642344770
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 593
EP - 602
BT - Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
ER -