Concerning the problem that the recognition algorithm which was obtained under one vein image dataset is lack of universality to other datasets, a Siamese Nonnegative Matrix Factorization (NMF) model with transferability was proposed. Firstly, the supervised learning for the vein images with same labels in the source dataset was achieved by using two NMF models with the same structures and the parameter sharing. Then, the vein feature differences between two different datasets were reduced through using maximum mean discrepancy constraint, that is to transfer the knowledge in the source dataset to the target dataset. Finally, the matching of vein images was realized based on cosine distance. Experimental results show that, the proposed recognition algorithm can not only achieve the high recognition accuracy on the source dataset, but also respectively reduce the average False Accept Rate (FAR) and average False Reject Rate (FRR) to 0.043 and 0.055 on the target dataset when using only a small number of vein images in the target dataset. In addition, the average recognition time of the proposed algorithm is 0.56 seconds, which can meet the real-time requirement of recognition.

Facing with multi-category samples with limited number of annotations,in order to improve vehicle face recognition accuracy,a vehicle face recognition algorithm based on improved Nonnegative Matrix Factorization(NMF)was proposed. Firstly,the shape feature of local region of vehicle face image was extracted by Histogram of Oriented Gradients (HOG)operator,which was used as the original feature of vehicle face image. Then,the NMF model with multiple weights, orthogonality and sparse constraints was proposed,based on which,the feature bases describing the vehicle face image key regions were acquired,and the feature dimension reduction was achieved. Finally,the discrete cosine distance was used to calculate the similarity between features,and it was able to be concluded that whether the vehicle face images were matched or not. Experimental results show that the proposed recognition algorithm can obtain good recognition effect with accuracy of 97. 68% on the established vehicle face image dataset,at the same time,the proposed algorithm can meet the real-time requirement.

In order to improve the effectiveness of vein feature extraction, a dorsal hand vein recognition method based on sparse coding was proposed. Firstly, during image acquisition process, acquisition system parameters were adaptively adjusted in real-time according to image quality assessment results, and the vein image with high quality could be acquired. Then concerning that the effectiveness of subjective vein feature mainly depends on experience, a feature learning mechanism based on sparse coding was proposed, thus high-quality objective vein features could be extracted. Experiments show that vein features obtained by the proposed method have good inter-class separableness and intra-class compactness, and the system using this algorithm has a high recognition rate.