Abstract
Palmprint biometrics is a promising modality that enables efficient human identification, also in a mobile scenario. In this paper, a novel approach to feature extraction for palmprint verification is presented. The features are extracted from hand geometry and palmprint texture and fused. The use of a fusion of features facilitates obtaining a higher accuracy and, at the same time, provides more robustness to intrusive factors like illumination, variation, or noise. The major contribution of this paper is the proposition and evaluation of a lightweight verification schema for biometric systems that improves the accuracy without increasing computational complexity which is a necessary requirement in real-life scenarios.
Highlights
Biometric identification systems are becoming increasingly popular and have been widely researched recently. ey are applied as security systems, for example, detecting suspects in a crowd and finding out the identity of a person entering a plane or a restricted area. e key advantages of biometrics [1] are as follows: it is not possible to forget any token, it is not required to carry any additional items, and the same biometric feature may be used in numerous cases. us, biometrics is userfriendly, and new methods and emerging modalities are still being proposed [2]
Biometrics may be based on numerous traits such as fingerprint, palmprint, iris, voice, gait, and many others [3]. ey can be either anatomical such as ear biometrics [4] and lips recognition [5] or behavioral such as keystroke dynamics [6] or mouse clicks [7]
There are the Hough transform used in [18], the Haar discrete wavelet transform implemented in [19], and the discrete cosine transform used in [20]. ere are some local descriptors applied to feature extraction: local binary patterns [21], SURF and SIFT descriptors [22], and histogram of oriented diagrams used in our previous work [16]. Another popular method is based on statistical principal component analysis (PCA) implemented in [23, 24], which was presented, for example, in [25,26,27]
Summary
Biometric identification systems are becoming increasingly popular and have been widely researched recently. ey are applied as security systems, for example, detecting suspects in a crowd and finding out the identity of a person entering a plane or a restricted area. e key advantages of biometrics [1] are as follows: it is not possible to forget any token (as those tokens are parts of body or behaviour!), it is not required to carry any additional items (such as keys and badges), and the same biometric feature may be used in numerous cases (e.g., in biometric passport, in a local sport center, and to unlock a smartphone). us, biometrics is userfriendly, and new methods and emerging modalities are still being proposed [2]. Ere are some local descriptors applied to feature extraction: local binary patterns [21], SURF and SIFT descriptors [22], and histogram of oriented diagrams used in our previous work [16] Another popular method is based on statistical principal component analysis (PCA) implemented in [23, 24], which was presented, for example, in [25,26,27]. GLCM is a method that can be used in order to discover information about the statistical distribution of the intensities and about the relative position of neighbourhood pixels of the analyzed image as well After feature extraction, they are classified by SVM, giving the highest result equal to 98.25%. E middle point and distance d were calculated between points 2 and 6. en, the angle between these two points was found, and the whole image was rotated by this angle
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
