Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Personal authentication using hand images
Pattern Recognition Letters
A survey of biometric technology based on hand shape
Pattern Recognition
Personal verification using palmprint and hand geometry biometric
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
IEEE Transactions on Image Processing
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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.