103 research outputs found
Deep learning approach for Touchless Palmprint Recognition based on Alexnet and Fuzzy Support Vector Machine
Due to stable and discriminative features, palmprint-based biometrics has been gaining popularity in recent years. Most of the traditional palmprint recognition systems are designed with a group of hand-crafted features that ignores some additional features. For tackling the problem described above, a Convolution Neural Network (CNN) model inspired by Alex-net that learns the features from the ROI images and classifies using a fuzzy support vector machine is proposed. The output of the CNN is fed as input to the fuzzy Support vector machine. The CNN\u27s receptive field aids in extracting the most discriminative features from the palmprint images, and Fuzzy SVM results in a robust classification. The experiments are conducted on popular contactless datasets such as IITD, POLYU2, Tongji, and CASIA databases. Results demonstrate our approach outperformers several state-of-art techniques for palmprint recognition. Using this approach, we obtain 99.98% testing accuracy for the Tongji dataset and 99.76 % for the POLYU-II datasets
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
Palm print verification based deep learning
In this paper, we consider a palm print characteristic which has taken wide attentions in recent studies. We focused on palm print verification problem by designing a deep network called a palm convolutional neural network (PCNN). This network is adapted to deal with two-dimensional palm print images. It is carefully designed and implemented for palm print data. Palm prints from the Hong Kong Polytechnic University Contact-free (PolyUC) 3D/2D hand images dataset are applied and evaluated. The results have reached the accuracy of 97.67%, this performance is superior and it shows that our proposed method is efficient
Palm print recognition based on harmony search algorithm
Due to its stabilized and distinctive properties, the palmprint is considered a physiological biometric. Recently, palm print recognition has become one of the foremost desired identification methods. This manuscript presents a new recognition palm print scheme based on a harmony search algorithm by computing the Gaussian distribution. The first step in this scheme is preprocessing, which comprises the segmentation, according to the characteristics of the geometric shape of palmprint, the region of interest (ROI) of palmprint was cut off. After the processing of the ROI image is taken as input related to the harmony search algorithm for extracting the features of the palmprint images through using many parameters for the harmony search algorithm, Finally, Gaussian distribution has been used for computing distance between features for region palm print images, in order to recognize the palm print images for persons by training and testing a set of images, The scheme which has been proposed using palmprint databases, was provided by College of Engineering Pune (COEP), the Hong Kong Polytechnic University (HKPU), Experimental results have shown the effectiveness of the suggested recognition system for palm print with regards to the rate of recognition that reached approximately 92.60%
- ā¦