Fusing multiple features within one biometric modality has attracted increasing attention and interest among researchers during recent decades because the concept is useful in addressing a wide range of real world problems. In this paper, we propose a novel fusion approach that combines two feature extraction algorithms: Local Binary Pattern Histogram Fourier Features (LBP-HF) and Gabor filter technique for use as one feature extraction. The fused features are applied to improve the performance of palmprint recognition. However, the main problem associated with this approach is the extremely large number of features, which can result in an overfitting problem for classification. To overcome this difficulty, spectral regression kernel discriminant analysis (SR-KDA) is applied as a dimensionality reduction technique. When designing the proposed recognition system, the k-nearest neighbour (KNN) classifier is used for the final decision. The performance of the proposed approach was evaluated using the challenging multispectral palmprint PolyU database. From the experimental results, it can be suggested that the system presented consistently yields significant performance gains compared to the state-of-the art methods.
['Wafa El-Tarhouni', 'Larbi Boubchir', 'Noor Al-Maadeed', 'Mosa Elbendak', 'Ahmed Bouridane']
Multispectral palmprint recognition based on local binary pattern histogram fourier features and gabor filter