Contactless 2D fingerprint identification is more hygienic, and enables deformation free imaging for higher accuracy. Success of such emerging contactless fingerprint technologies requires advanced capabilities to accurately match such fingerprint images with the conventional fingerprint databases which have been developed and deployed in last two decades. Convolutional neural networks have shown remarkable success for the face recognition problem. However, there has been very few attempts to develop CNN-based methods to address challenges in fingerprint identification problems. This paper proposes a multi-Siamese CNN architecture for accurately matching contactless and contact-based fingerprint images. In addition to the fingerprint images, hand-crafted fingerprint features, e.g. minutiae and core point, are also incorporated into the proposed architecture. This multi-Siamese CNN is trained using the fingerprint images and extracted features. Therefore, a more robust deep fingerprint representation is formed from the concatenation of deep feature vectors generated from multi-networks. In order to demonstrate the effectiveness of the proposed approach, a publicly available database consisting of contact-based and respective contactless finger-prints is utilized. The experimental evaluations presented in this paper achieve outperforming results, over other CNN-based methods and the traditional fingerprint cross matching methods, and validate our approach.