Multispectral Palmprint Encoding and Recognition

Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z. Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral Palmprint Encoding for Human Recognition", International Conference on Computer Vision, 2011. MATLAB Code available: https://sites.google.com/site/zohaibnet/Home/code

PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features

Designing an end-to-end deep learning network to match the biometric features with limited training samples is an extremely challenging task. To address this problem, we propose a new way to design an end-to-end deep CNN framework i.e., PVSNet that works in two major steps: first, an encoder-decoder network is used to learn generative domain-specific features followed by a Siamese network in which convolutional layers are pre-trained in an unsupervised fashion as an autoencoder. The proposed model is trained via triplet loss function that is adjusted for learning feature embeddings in a way that minimizes the distance between embedding-pairs from the same subject and maximizes the distance with those from different subjects, with a margin. In particular, a triplet Siamese matching network using an adaptive margin based hard negative mining has been suggested. The hyper-parameters associated with the training strategy, like the adaptive margin, have been tuned to make the learning more effective on biometric datasets. In extensive experimentation, the proposed network outperforms most of the existing deep learning solutions on three type of typical vein datasets which clearly demonstrates the effectiveness of our proposed method.Comment: Accepted in 5th IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), 2019, Hyderabad, Indi

Vascular Innovation Science

Vascular Innovation science are a standout amongst the most approaching innovations which is very secure. In this paperi introduces a study of machine learning techniques utilized for Vascular innovation development applications, and distinguishes significant exploration issues. Basic mission assets and applications require instruments to recognize when honest to goodness clients attempt to abuse their benefits; unquestionably biometrics serves to give such administrations

Multi-modal palm-print and hand-vein biometric recognition at sensor level fusion

When it is important to authenticate a person based on his or her biometric qualities, most systems use a single modality (e.g. fingerprint or palm print) for further analysis at higher levels. Rather than using higher levels, this research recommends using two biometric features at the sensor level. The Log-Gabor filter is used to extract features and, as a result, recognize the pattern, because the data acquired from images is sampled at various spacing. Using the two fused modalities, the suggested system attained greater accuracy. Principal component analysis (PCA) was performed to reduce the dimensionality of the data. To get the optimum performance between the two classifiers, fusion was performed at the sensor level utilizing different classifiers, including K-nearest neighbors (K-NN) and support vector machines (SVMs). The technology collects palm prints and veins from sensors and combines them into consolidated images that take up less disk space. The amount of memory needed to store such photos has been lowered. The amount of memory is determined by the number of modalities fused

LEARNING-FREE DEEP FEATURES FOR MULTISPECTRAL PALM-PRINT CLASSIFICATION

The feature extraction step is a major and crucial step in analyzing and understanding raw data as it has a considerable impact on the system accuracy. Unfortunately, despite the very acceptable results obtained by many handcrafted methods, they can have difficulty representing the features in the case of large databases or with strongly correlated samples. In this context, we proposed a new, simple and lightweight method for deep feature extraction. Our method can be configured to produce four different deep features, each controlled to tune the system accuracy. We have evaluated the performance of our method using a multispectral palmprint based biometric system and the experimental results, using the CASIA database, have shown that our method has high accuracy compared to many current handcrafted feature extraction methods and many well known deep learning based methods

A Framework for Verification in Contactless Secure Physical Access Control and Authentication Systems

Biometrics is one of the very popular techniques in user identification for accessing institutions and logging into attendance systems. Currently, some of the existing biometric techniques such as the use of fingerprints are unpopular due to COVID-19 challenges. This paper identifies the components of a framework for secure contactless access authentication. The researcher selected 50 journals from Google scholar which were used to analyze the various components used in a secure contactless access authentication framework. The methodology used for research was based on the scientific approach of research methodology that mainly includes data collection from the 50 selected journals, analysis of the data and assessment of results. The following components were identified: database, sensor camera, feature extraction methods, matching and decision algorithm. Out of the considered journals the most used is CASIA database at 40%, CCD Sensor camera with 56%, Gabor feature extraction method at 44%, Hamming distance for matching at 100% and PCA at 100% was used for decision making. These findings will assist the researcher in providing a guide on the best suitable components. Various researchers have proposed an improvement in the current security systems due to integrity and security problems

A Method for Obtaining Electronic Voting Systems based Voter Confidentiality and Voting Accuracy

A Voting is common in our daily life, from electing president to electing committee. A complete electronic voting scheme suitable for all kinds of voting with safe guaranty where the voter?s privacy can be protected. Fingerprint system security have been investigated, including the use of fake fingerprints for masquerading identity, the problem of fingerprint alteration or obfuscation has received very little attention. Fingerprint image quality assessment software (e.g., NFIQ) cannot always detect altered fingerprints since the implicit image quality due to alteration may not change significantly. The main contributions of this Research are-1.Compiling case studies of incidents where individuals were found to have altered their fingerprints for circumventing AFIS.2.Identifying the damages of fingerprint alteration on the accuracy of a commercial fingerprint matcher.3.Classifying the alterations into three major categories and suggesting possible countermeasures.4.Developing a technique to automatically detect altered fingerprints based on analyzing orientation field and minutiae distribution.5.Evaluating the proposed technique and the NFIQ algorithm on a big database of altered fingerprints provided by a law enforcement agency. Experimental results show the feasibility of the proposed approach in detecting altered fingerprints and highlight the need to further pursue this problem