116 research outputs found
Palmprint Gender Classification Using Deep Learning Methods
Gender identification is an important technique that can improve the performance of authentication systems by reducing searching space and speeding up the matching process. Several biometric traits have been used to ascertain human gender. Among them, the human palmprint possesses several discriminating features such as principal-lines, wrinkles, ridges, and minutiae features and that offer cues for gender identification. The goal of this work is to develop novel deep-learning techniques to determine gender from palmprint images. PolyU and CASIA palmprint databases with 90,000 and 5502 images respectively were used for training and testing purposes in this research. After ROI extraction and data augmentation were performed, various convolutional and deep learning-based classification approaches were empirically designed, optimized, and tested. Results of gender classification as high as 94.87% were achieved on the PolyU palmprint database and 90.70% accuracy on the CASIA palmprint database. Optimal performance was achieved by combining two different pre-trained and fine-tuned deep CNNs (VGGNet and DenseNet) through score level average fusion. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) was also implemented to ascertain which specific regions of the palmprint are most discriminative for gender classification
Gender and Ethnicity Classification based on Palmprint and Palmar Hand Images from Uncontrolled Environment
Soft biometric attributes such as gender, ethnicity or age may provide useful
information for biometrics and forensics applications. Researchers used, e.g.,
face, gait, iris, and hand, etc. to classify such attributes. Even though hand
has been widely studied for biometric recognition, relatively less attention
has been given to soft biometrics from hand. Previous studies of soft
biometrics based on hand images focused on gender and well-controlled imaging
environment. In this paper, the gender and ethnicity classification in
uncontrolled environment are considered. Gender and ethnicity labels are
collected and provided for subjects in a publicly available database, which
contains hand images from the Internet. Five deep learning models are
fine-tuned and evaluated in gender and ethnicity classification scenarios based
on palmar 1) full hand, 2) segmented hand and 3) palmprint images. The
experimental results indicate that for gender and ethnicity classification in
uncontrolled environment, full and segmented hand images are more suitable than
palmprint images.Comment: Accepted in the International Joint Conference on Biometrics (IJCB
2020), scheduled for Sep 28-Oct 1, 202
Palmprint Recognition in Uncontrolled and Uncooperative Environment
Online palmprint recognition and latent palmprint identification are two
branches of palmprint studies. The former uses middle-resolution images
collected by a digital camera in a well-controlled or contact-based environment
with user cooperation for commercial applications and the latter uses
high-resolution latent palmprints collected in crime scenes for forensic
investigation. However, these two branches do not cover some palmprint images
which have the potential for forensic investigation. Due to the prevalence of
smartphone and consumer camera, more evidence is in the form of digital images
taken in uncontrolled and uncooperative environment, e.g., child pornographic
images and terrorist images, where the criminals commonly hide or cover their
face. However, their palms can be observable. To study palmprint identification
on images collected in uncontrolled and uncooperative environment, a new
palmprint database is established and an end-to-end deep learning algorithm is
proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1)
contains 7881 images from 2035 palms collected from the Internet. The proposed
algorithm consists of an alignment network and a feature extraction network and
is end-to-end trainable. The proposed algorithm is compared with the
state-of-the-art online palmprint recognition methods and evaluated on three
public contactless palmprint databases, IITD, CASIA, and PolyU and two new
databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental
results showed that the proposed algorithm outperforms the existing palmprint
recognition methods.Comment: Accepted in the IEEE Transactions on Information Forensics and
Securit
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