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. 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. We validated the innovative PalmNet on several palmprint databases captured using different touchless acquisition procedures and heterogeneous devices, and in all cases, a recognition accuracy greater than that of the current methods in the literature was obtained.
PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition / A. Genovese, V. Piuri, K.N. Plataniotis, F. Scotti. - In: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY. - ISSN 1556-6013. - 14:12(2019), pp. 3160-3174.
|Titolo:||PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition|
|Parole Chiave:||Palmprint; Touchless Biometrics; Less-constrained; CNN; Deep Learning; PCA; Gabor|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
|Progetto:||COntactlesS Multibiometric mObile System in the wild: COSMOS|
|Data di pubblicazione:||2019|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/TIFS.2019.2911165|
|Appare nelle tipologie:||01 - Articolo su periodico|