The project

Touchless palmprint recognition systems enable high-accuracy recognition of individuals through less-constrained and highly usable procedures that do not require the contact of the palm with a surface. To perform this recognition, methods based on local texture descriptors and Convolutional Neural Networks (CNNs) are currently used to extract highly discriminative features while compensating for variations in scale, rotation, and illumination in biometric samples. In particular, the main advantage of CNN-based methods is their ability to adapt to biometric samples captured with heterogeneous devices. However, the current methods rely on either supervised training algorithms, which require class labels (e.g., the identities of the individuals) during the training phase, or filters pretrained on general-purpose databases, which may not be specifically suitable for palmprint data.

Another advantage of is touchless palmprint recognition is that with a palmar hand acquisition, it is possible to extract the palmprint as well as the Inner Finger Texture (IFT) and increase the recognition accuracy without requiring further biometric acquisitions. However, current methods based on Deep Learning (DL) do not consider the fusion of palmprint with IFT.

The method

PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition

To achieve a high recognition accuracy with touchless palmprint samples captured using different devices while neither requiring class labels for training nor using pretrained filters, we introduce PalmNet, which is a novel CNN that uses a newly developed method to tune palmprint-specific filters through an unsupervised procedure based on Gabor responses and Principal Component Analysis (PCA), not requiring class labels during training. PalmNet is a new method of applying Gabor filters in a CNN and is designed to extract highly discriminative palmprint-specific descriptors and to adapt to heterogeneous databases.

Touchless Palmprint and Finger Texture Recognition: A Deep Learning Fusion Approach

We propose the first novel method in the literature based on a CNN to perform the fusion of palmprint and IFT using a single hand acquisition. Our approach uses an innovative procedure based on training the same CNN topology separately on the palmprint and the IFT, adapting the neural model to the different biometric traits, and then performing a feature-level fusion.

Related publications

  • A. Genovese, V. Piuri, K. N. Plataniotis, and F. Scotti, "PalmNet: Gabor-PCA Convolutional Networks for touchless palmprint recognition", in IEEE Transactions on Information Forensics and Security, vol. 14, no. 2, December, 2019, pp. 3160-3174. ISSN: 1556-6013. [DOI: 10.1109/TIFS.2019.2911165] [PDF] [BibTex entry]

  • A. Genovese, V. Piuri, F. Scotti, and S. Vishwakarma, "Touchless palmprint and finger texture recognition: A Deep Learning fusion approach", in Proc. of the 2019 IEEE Int. Conf. on Computational Intelligence & Virtual Environments for Measurement Systems and Applications (CIVEMSA 2019), Tianjin, China, June 14-16, 2019, pp. 1-6. ISBN: 978-1-5386-8344-6. [DOI: 10.1109/CIVEMSA45640.2019.9071620] [PDF] [BibTex entry]


@Article {tifs19,
    author = {A. Genovese and V. Piuri and K. N. Plataniotis and F. Scotti},
    title = {PalmNet: Gabor-PCA Convolutional Networks for touchless palmprint recognition},
    journal = {IEEE Transactions on Information Forensics and Security},
    volume = {14},
    number = {2},
    pages = {3160-3174},
    month = {December},
    year = {2019},
    note = {1556-6013}

@InProceedings {civemsa19,
    author = {A. Genovese and V. Piuri and F. Scotti and S. Vishwakarma},
    booktitle = {Proc. of the 2019 IEEE Int. Conf. on Computational Intelligence & Virtual Environments
    for Measurement Systems and Applications (CIVEMSA 2019)},
    title = {Touchless palmprint and finger texture recognition: A Deep Learning fusion approach},
    address = {Tianjin, China},
    month = {June},
    day = {14-16},
    year = {2019},
    pages = {1-6},


This work was supported in part by the Italian Ministry of Research as part of the MIUR PRIN project COSMOS (201548C5NT). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research, within the project "Deep Learning and CUDA for advanced and less-constrained biometric systems".



Source code (Matlab):