Multibiometric recognition has become one of the most important solutions for enhancing overall personal recognition performance due to several inherent limitations of unimodal biometrics, such as nonuniversality and unacceptable reliability. However, most existing multibiometrics fuse completely different biometric traits based on addition schemes, which usually require several sensors and make the final feature sets large. In this paper, we propose a joint multi-instance hand-based biometric feature learning method for biometric recognition. Specifically, we first exploit the important direction data from multi-instance biometric images. Then, we simultaneously learn the discriminative features of multi-instance biometric traits and exploit the collaborative representations of multi-instance biometric features such that the final joint multi-instance feature descriptor is compact. Moreover, the importance weights of different biometric instances can be adaptively learned. Experimental results on the baseline multi-instance finger-knuckle-print and palmprint databases demonstrate the promising effectiveness of the proposed method.

由于单峰生物特征的一些固有局限性，例如非大学性和不可接受的可靠性，多生物特征识别已成为增强整体个人识别性能的最重要解决方案之一。但是，大多数现有的多生物特征融合了基于加法的完全不同的生物特征，这通常需要多个传感器并使最终的特征集变大。在本文中，我们提出了一种基于多实例手的联合生物特征识别学习方法。具体来说，我们首先利用多实例生物特征图像中的重要方向数据。然后，我们同时学习区分多实例生物特征的特征，并利用多实例生物特征的协作表示，以使最终的联合多实例特征描述符紧凑。此外，可以自适应地学习不同生物特征实例的重要性权重。在基线多实例指关节指纹和掌纹数据库上的实验结果证明了该方法的有希望的有效性。