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2 Annals of Computer Science and Information Systems

2 2019

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Weighted multimodal biometric recognition algorithm based on histogram of contourlet oriented gradient feature description

Zhang Xinman, Cheng Dongxu, Xu Xuebin

Annals of Computer Science and Information Systems

2019

Vol. 18

381–-384

Although the unimodal biometric recognition (such as face and palmprint) has higher convenience, its security is also relatively weak. The recognition accuracy is easy affected by many factors such as ambient light and recognition distance etc. To address this issue, we present a weighted multimodal biometric recognition algorithm with face and palmprint based on histogram of contourlet oriented gradient (HCOG) feature description. We employ the nonsubsampled contour transform (NSCT) to decompose the face and palmprint images, and the HOG method is adopted to extract the feature, which is named as HCOG feature. Then the dimension reduction process is applied on the HCOG feature and a novel weight value computation method is proposed to accomplish the multimodal biometric fusion recognition. Extensive experiments illustrate that our proposed weighted fusion recognition can achieve excellent recognition accuracy rates and outmatches the unimodal biometric recognition methods.

Palmprint recognition based on convolutional neural network-Alexnet

Gong Weiyong, Zhang Xinman, Deng Bohua, Xu Xuebin

Annals of Computer Science and Information Systems

2019

Vol. 18

313–-316

In the classic algorithm, palmprint recognition requires extraction of palmprint features before classification and recognition, which will affect the recognition rate. To solve this problem, this paper uses the convolutional neural network (CNN) structure Alexnet to realize palmprint recognition. First, according to the characteristics of the geometric shape of palmprint, the ROI area of palmprint was cut out. Then the ROI area after processing is taken as input layer of convolutional neural network. Next the PRelu activation function is used to train the network to select the best learning rate and super parameters. Finally, the palmprint was classified and identified. The method was applied to PolyU Multi-Spectral Palmprint Image Database and PolyU 2D+3D Palmprint Database, and the recognition rate of a single spectrum was up to 99.99%.