Rotation and Translation Invariant Palmprint Recognition With Biologically Inspired Transform

Abstract

Extracting rotation and translation invariant features is a difficult task for palmprint recognition. Traditional methods have difficulty in dealing with palmprint images degraded by those variations. Studies have shown that neurons at higher levels exhibit an increasing degree of invariance to above mentioned image variations. Moreover, primary visual cortex(V1) is believed to give stronger responses to light bars of certain directions. Based on these observations, a biologically inspired transform feature extractor, namely BIT, for palmprint recognition is proposed in this paper. BIT involves two stages, which mimics visual information processing in V1. In the first stage, we build an orientation edge detector to highlight the edges response in each direction. The orientation edge detector is primarily composed of a phase congruency based edge detector and a bipolar filter. After that, a local spatial frequency detector produces a response, converting rotation factors of orientation edges into a horizontal shifted map. In the second stage, the orientation edge detector and local spatial frequency detector are applied again, which converts shifted map into an invariant pixel in feature map. Extensive experimental results not only show that our method is robust to image variations including rotation and translation, but also illustrate the effectiveness and discriminability of the extracted invariant palmprint features in recognition problems. INDEX TERMS Biometrics, image filter, feature extraction, palmprint recognition, biologically inspired transform.

Robust features fusion utilization for supervised palmprint recognition

“…Zhou et al have solved the issue of variance in encoding, rotation, and scaling that would adversely affect the accuracy of the palmprint recognition 15 …”

“…14 Zhou et al have solved the issue of variance in encoding, rotation, and scaling that would adversely affect the accuracy of the palmprint recognition. 15 Sasirekha et al have proposed a fingerprint recognition framework. Authors have extracted the features based on invariant moments.…”

Improved Siamese Palmprint Authentication Using Pre-Trained VGG16-Palmprint and Element-Wise Absolute Difference

“…Palmprint, in nature, is more secure and private than other biometric authentication, such as face or iris, which are publicly available on social media as most people share their pictures. Therefore, palmprint has emerged as a viable area of use in the real world [8]. The contact-based biometrics have several drawbacks; these drawbacks include contaminated patterns that are caused by the latent remains in the scanner.…”

Palmprint-Palmvein Fusion Recognition Based on Deep Hashing Network

“…For different palmprint modalities, different methods have been proposed e.g. 2D palmprint recognition [2,3], 3D palmprint recognition [4], palmprint and vein recognition [5,6], etc. The existing methods can be briefly divided into the subspace-based [7][8][9], statistic-based [10][11][12], deep-learningbased [13,14] and coding-based methods [15,16].…”