Personal recognition using hand shape and texture

• Published in: IEEE transactions on image processing Vol. 15; no. 8; pp. 2454 - 2461
• Main Authors: Kumar, A., Zhang, D.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.2006; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Biometrics; Biometry - methods; Classification tree analysis; Computer Security; Decision trees; Detection, estimation, filtering, equalization, prediction; Digital cameras; Discrete cosine transforms; Exact sciences and technology; feature level fusion; Feature recognition; feature subset selection and combination; Hand - anatomy & histology; hand-shape recognition; Hardware; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Information Storage and Retrieval - methods; Information, signal and communications theory; Mathematical models; Miscellaneous; Palm; palmprint recognition; Pattern recognition; Pattern Recognition, Automated - methods; Predictive models; Recognition; Shape; Signal and communications theory; Signal processing; Signal, noise; Studies; Support vector machine classification; Support vector machines; Surface layer; Telecommunications and information theory; Texture; User-Computer Interface; Biometrics; Performance evaluation; feature subset selection and combination; Support vector machine; Pattern recognition; Decision tree; Signal classification; Texture; Selection problem; palmprint recognition; Model matching; Signal processing; Feature extraction; System identification; Automatic recognition; Discrete cosine transforms; hand-shape recognition; feature level fusion
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2006.875214

This paper proposes a new bimodal biometric system using feature-level fusion of hand shape and palm texture. The proposed combination is of significance since both the palmprint and hand-shape images are proposed to be extracted from the single hand image acquired from a digital camera. Several new hand-shape features that can be used to represent the hand shape and improve the performance are investigated. The new approach for palmprint recognition using discrete cosine transform coefficients, which can be directly obtained from the camera hardware, is demonstrated. None of the prior work on hand-shape or palmprint recognition has given any attention on the critical issue of feature selection. Our experimental results demonstrate that while majority of palmprint or hand-shape features are useful in predicting the subjects identity, only a small subset of these features are necessary in practice for building an accurate model for identification. The comparison and combination of proposed features is evaluated on the diverse classification schemes; naive Bayes (normal, estimated, multinomial), decision trees (C4.5, LMT), /spl kappa/-NN, SVM, and FFN. Although more work remains to be done, our results to date indicate that the combination of selected hand-shape and palmprint features constitutes a promising addition to the biometrics-based personal recognition systems.