ÐA work in defining and implementing a biometric system based on hand geometry identification is presented here. Hand features are extracted from a color photograph taken when the user has placed his hand on a platform designed for such a task. Different pattern recognition techniques have been tested to be used in classification and/or verification from Euclidean distance to neural networks. Experimental results, up to a 97 percent rate of success in classification, will show the possibility of using this system in medium/high security environments with full acceptance from all users.

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In this article, a gait recognition algorithm is presented based on the information obtained from inertial sensors embedded in a smartphone, in particular, the accelerometers and gyroscopes typically embedded on them. The algorithm processes the signal by extracting gait cycles, which are then fed into a Recurrent Neural Network (RNN) to generate feature vectors. To optimize the accuracy of this algorithm, we apply a random grid hyperparameter selection process followed by a hand-tuning method to reach the final hyperparameter configuration. The different configurations are tested on a public database with 744 users and compared with other algorithms that were previously tested on the same database. After reaching the best-performing configuration for our algorithm, we obtain an equal error rate (EER) of 11.48% when training with only 20% of the users. Even better, when using 70% of the users for training, that value drops to 7.55%. The system manages to improve on state-of-the-art methods, but we believe the algorithm could reach a significantly better performance if it was trained with more visits per user. With a large enough database with several visits per user, the algorithm could improve substantially.

Human recognition on smartphone devices for unlocking, online payment, and bank account verification is one of the significant uses of biometrics. The exponential development and integration of this technology have been established since the introduction in 2013 of the fingerprint mounted sensor in the Apple iPhone 5s by Apple Inc.© (Motorola© Atrix was previously launched in 2011). Nowadays, in the commercial world, the main biometric variants integrated into mobile devices are fingerprint, facial, iris, and voice. In 2019, LG© Electronics announced the first mobile exhibiting vascular biometric recognition, integrated using the palm vein modality: LG© G8 ThinQ (hand ID). In this work, in an attempt to become the first research-embedded approach to smartphone vein identification, a novel wrist vascular biometric recognition is designed, implemented, and tested on the Xiaomi© Pocophone F1 and the Xiaomi© Mi 8 devices. The near-infrared camera mounted for facial recognition on these devices accounts for the hardware employed. Two software algorithms, TGS-CVBR® and PIS-CVBR®, are designed and applied to a database generation and the identification task, respectively. The database, named UC3M-Contactless Version 2 (UC3M-CV2), consists of 2400 contactless infrared images from both wrists of 50 different subjects (25 females and 25 males, 100 individual wrists in total), collected in two separate sessions with different environmental light conditions. The vein biometric recognition, using PIS-CVBR®, is based on the SIFT®, SURF®, and ORB algorithms. The results, discussed according to the ISO/IEC 19795-1:2019 standard, are promising and pave the way for contactless real-time-processing wrist recognition on smartphone devices. INDEX TERMS vein biometric recognition, smartphone, wrist vascular biometric recognition, contactless database, biometrics on mobile devices, near-infrared camera, Xiaomi© Pocophone F1, Xiaomi© Mi 8, SIFT® (Scale-Invariant Feature Transform), SURF® (Speeded Up Robust Features).

Abstract. Among all the biometric techniques known nowadays, IrisRecognition is taken as the most promising of all, due to its low error rates without being invasive and with low relation to police records. Based on Daugman s work, the authors have developed their own Iris Recognition system, obtaining results that show the performance of the prototype and proves the excellences of the system initially developed by Daugman. A full coverage of the pre-processing and feature extraction blocks is given. Special efforts have been applied in order to obtain low template sizes and fast verification algorithms. This effort is intended to enable a human authentication in small embedded systems, such as an Integrated Circuit Card (smart cards). The final results show viability of this target, enabling a template size down to 256 bits. Future works will be focussed in new feature extraction algorithms, as well as optimising the pre-processing block.

The utilisation of biometrics in mobile scenarios is increasing remarkably. At the same time, handwritten signature recognition is one of the modalities with highest potential of use for those applications where customers are used to sign in those traditional processes. However, several improvements have to be made in order to reach acceptable levels of performance, reliability and interoperability. The evaluation carried out in this study contributes with multiple results obtained from 43 users signing 60 times, divided in three sessions, in eight different capture devices, being six of them mobile devices and the other two digitisers specially made for signing and used as a baseline. At each session, a total of 20 signatures per user are captured by each device, so that the evaluation here reported a total of 20 640 signatures, stored in ISO/IEC 19794-7 format. The algorithm applied is a DTW-based one, particularly modified for mobile environments. The results analysed include interoperability, visual feedback and modality tests. One of the big challenges of this research was to discover if the handwritten signature modality in mobile devices should be split into two different modalities, one for those cases when the signature is performed with a stylus, and another when the fingertip is used for signing. Many relevant conclusions have been collected and, over all, multiple improvements have been reached contributing to future deployments of biometrics in mobile environments.

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