A survey of palmprint recognition

In this paper, a novel heuristic structure optimization methodology for radial basis probabilistic neural networks (RBPNNs) is proposed. First, a minimum volume covering hyperspheres (MVCH) algorithm is proposed to select the initial hidden-layer centers of the RBPNN, and then the recursive orthogonal least square algorithm (ROLSA) combined with the particle swarm optimization (PSO) algorithm is adopted to further optimize the initial structure of the RBPNN. The proposed algorithms are evaluated through eight benchmark classification problems and two real-world application problems, a plant species identification task involving 50 plant species and a palmprint recognition task. Experimental results show that our proposed algorithm is feasible and efficient for the structure optimization of the RBPNN. The RBPNN achieves higher recognition rates and better classification efficiency than multilayer perceptron networks (MLPNs) and radial basis function neural networks (RBFNNs) in both tasks. Moreover, the experimental results illustrated that the generalization performance of the optimized RBPNN in the plant species identification task was markedly better than that of the optimized RBFNN.

A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks

A review of biometric technology along with trends and prospects

Computer vision and deep learning techniques for pedestrian detection and tracking: A survey

Palmprint verification based on principal lines

Letters: Palmprint recognition using FastICA algorithm and radial basis probabilistic neural network

Letters: Noise removal using a novel non-negative sparse coding shrinkage technique

This paper proposes a novel method for recognizing palmprint using the winner-take-all (WTA) network based independent component analysis (ICA) algorithm and the radial basis probabilistic neural network (RBPNN) proposed by us. The WTA-ICA algorithm exploits the maximization of the sparse measure criterion as the cost function, and it extracts successfully palmprint features. The classification performance is implemented by the RBPNN. The RBPNN is trained by the orthogonal least square (OLS) algorithm and its structure is optimized by the recursive OLS (ROLS) algorithm. Experimental results show that the RBPNN achieves higher recognition rate and better classification efficiency with other usual classifiers.

Palmprint recognition using ICA based on winner-take-all network and radial basis probabilistic neural network

Deception detecting from speech signal using relevance vector machine and non-linear dynamics features

Immune K-SVD algorithm for dictionary learning in speech denoising

Li Shang

Papers

Letters: Palmprint recognition using FastICA algorithm and radial basis probabilistic neural network

Letters: Noise removal using a novel non-negative sparse coding shrinkage technique

Palmprint recognition using ICA based on winner-take-all network and radial basis probabilistic neural network

Deception detecting from speech signal using relevance vector machine and non-linear dynamics features

Immune K-SVD algorithm for dictionary learning in speech denoising