Paper: Multi-Weighted Co-Occurrence Descriptor Encoding for Vein Recognition
Despite being highly secure, vein recognition suffers from the high inter-class similarity and intra-class variation resulting from the uncontrolled image capture, making the design of discriminative and robust representation very important.
The recent success of convolutional neural network (CNN) for various image understanding tasks makes it a promising method for feature extraction. However, limited variability in small-scale datasets leads to systems derived from the direct training or fine-tuning not transferable and unreliable for practical biometric applications.
This motivates the design of a multi-weighted co-occurrence descriptor encoding (MWCDE) model for vein recognition. Instead of directly conducting a feed-forward operation with a pre-trained CNN for obtaining the semantic features from the fully connected layers, co-occurrence features among convolutional filters are modeled first in MWCDE by a simple convolution between an indicator filter in a higher layer with a to-be-reweighted filter in a lower layer, and a redundancy-driven indicator filter selection algorithm is designed for filtering out some ambiguous representations.
Second, another hard feature weighting strategy with a binary masking scheme is proposed for discarding noisy background and feature redundancy. The selected high-order descriptors are then embedded and aggregated into the compact feature vectors with a saliency driven spatial weighted Fisher vector algorithm, followed by the introduction of a generalized support vector machine for recognition.
Extensive experiments with three benchmark vein datasets demonstrate that the proposed framework can achieve state-of-the-art results, and an additional experiment with the PolyU multispectral palmprint database illustrates its generalization ability.
Code is available at GutHub here.
Wang, Guoqing; Sun, Changming; Sowmya, Arcot. Multi-weighted co-occurrence feature encoding for vein recognition. IEEE Transactions on Information Forensics and Security. 2020; 15:375-390.
Download the full paper here.
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