Biometric Identification through Hand Geometry Measurements
IEEE Transactions on Pattern Analysis and Machine Intelligence
Palmprint recognition using eigenpalms features
Pattern Recognition Letters
Combining implicit polynomials and geometric features for hand recognition
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fisherpalms based palmprint recognition
Pattern Recognition Letters
Integrating Shape and Texture for Hand Verification
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Personal authentication using hand images
Pattern Recognition Letters
An automated palmprint recognition system
Image and Vision Computing
Personal verification using palmprint and hand geometry biometric
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Handbook of Multibiometrics
Biometrics: a tool for information security
IEEE Transactions on Information Forensics and Security
Characterization of palmprints by wavelet signatures via directional context modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
Personal recognition using hand shape and texture
IEEE Transactions on Image Processing
Energy Efficient Distributed Face Recognition in Wireless Sensor Network
Wireless Personal Communications: An International Journal
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Presence of unique palmprint patterns makes palmprints suitable for personal recognition. In low resolution images, these patterns mainly consist of multisized and multidirectional principal lines and wrinkles. This prompted us to use wavelet based multiresolution analysis (MRA) to extract these features. Most of the palmprint feature extraction works based on wavelets fail to consider spatial location of energies. We have proposed a new method to extract these features in the form of spatially localized wavelet energy signatures (SLWES) which comprise spatial and frequency information at different resolutions. SLWES features characterize palmprints effectively and are represented in a vector form. We have also identified a few new hand geometrical features suitable for personal recognition, and thirty hand geometry features (including new) are extracted to form a geometrical feature vector. These feature vectors are then examined for their individual and combined (fusion) identification performances. We experimented these methods on our database and obtained 97.5% accuracy for combined mode, which is comparable with similar bimodal (geometrical and palmprints) identification methods.