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

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

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

A Review on an Authentication System using Secret Sharing

Security using Authentication system is an important concern in the field of information technology. It is an important thing as per as concern to the ruling of internet over people today. The growth in the usage of internet has increased the demand for fast and accurate user identification and authentication. This New threats, risks and vulnerabilities emphasize the need of a strong authentication system. The cryptography is a secret sharing scheme where a secret data gets divided into number of pieces called shares and not a single share discloses any information about secret data. There are some automated methods to identify and verify the user based on the physiological characteristics. To deal with such methods, there is a technology called biometrics which measures and statistically analyses the biological data. The biometric samples which are stored in the database as a secret are unique for each user so that no one can predict those samples. A biometric authentication system provides automatic authentication of an individual on the basis of unique features or characteristics possessed by an individual. The authentication system can be stronger using multiple factors for authentication process. The application like Aadhar Card uses more than one factor for authentication. There is some difficulty with authentication systems such as user privacy considerations in case of multiple biometric features, huge size databases and centralized database which may create security threats. To address such tribulations, the Authentication System using Secret Sharing is proposed, Secret sharing splits the centralized database across the different locations. This helps in reducing the database size and removal of threats in centralized database. Also user privacy is maintained due to the decentralized database

Data anonymization using pseudonym system to preserve data privacy

Data collection and storage in a large size is done on a routine basis in any company or organization. To this end, wireless network infrastructure and cloud computing are two widely-used tools. With the use of such services, less time is needed to attain the required output, and also managing the jobs will be simpler for users. General services employ a unique identifier for the aim of storing data in a digital database. However, it may be associated with some limitations and challenges. There is a link between the unique identifier and the data holder, e.g., name, address, Identity card number, etc. Attackers can manipulate a unique identifier for stealing the whole data. To get the data needed, attackers may even eavesdrop or guess. It results in lack of data privacy protection. As a result, it is necessary to take into consideration the data privacy issues in any data digital data storage. With the use of current services, there is a high possibility of exposure and leak of data/information to an unauthorized party during their transfer process. In addition, attacks may take place against services; for instance spoofing attacks, forgery attacks, etc. in the course of information transaction. To address such risks, this paper suggests the use of a biometric authentication method by means of a palm vein during the authentication process. Furthermore, a pseudonym creation technique is adopted to make the database record anonymous, which can make sure the data is properly protected. This way, any unauthorized party cannot gain access to data/information. The proposed system can resolve the information leaked, the user true identity is never revealed to others