Designing reliable user authentication on mobile phones is becoming an increasingly important task to protect users' private information and data. Since biometric approaches can provide many advantages over the traditional authentication methods, they have become a significant topic for both academia and industry. The major goal of biometric user authentication is to authenticate legitimate users and identify impostors based on physiological and behavioral characteristics. In this paper, we survey the development of existing biometric authentication techniques on mobile phones, particularly on touch-enabled devices, with reference to 11 biometric approaches (five physiological and six behavioral). We present a taxonomy of existing efforts regarding biometric authentication on mobile phones and analyze their feasibility of deployment on touch-enabled mobile phones. In addition, we systematically characterize a generic biometric authentication system with eight potential attack points and survey practical attacks and potential countermeasures on mobile phones. Moreover, we propose a framework for establishing a reliable authentication mechanism through implementing a multimodal biometric user authentication in an appropriate way. Experimental results are presented to validate this framework using touch dynamics, and the results show that multimodal biometrics can be deployed on touch-enabled phones to significantly reduce the false rates of a single biometric system. Finally, we identify challenges and open problems in this area and suggest that touch dynamics will become a mainstream aspect in designing future user authentication on mobile phones.

Surveying the Development of Biometric User Authentication on Mobile Phones

As a biometric modality, palmprints have been largely under-utilized, but they offer some advantages over fingerprints and facial biometrics. Recent improvements in imaging capabilities on handheld and wearable consumer devices have re-awakened interest in the use of palmprints. The aim of this paper is to provide a comprehensive review of state-of-the-art methods for palmprint recognition including Region of Interest extraction methods, feature extraction approaches and matching algorithms along with an overview of available palmprint datasets in order to understand the latest trends and research dynamics in the palmprint recognition field.

Toward Unconstrained Palmprint Recognition on Consumer Devices: A Literature Review

Contactless palmprint recognition systems alleviate the concerns on personal hygiene, acquisition flexibility, etc. Unfortunately, the preprocessing of contactless palmprint image faces several severe challenges, including unconstrained hand placement, complex background, light interference, etc. This paper proposes logical conjunction of triple-perpendicular-directional translation residual (TPDTR) for the improvement of contactless palmprint image preprocessing. The search of hand valley point is within the borders of hand valley gap detected by TPDTR, therefore, the computational cost is effectively decreased. Furthermore, the anti-interference capacity of region is stronger than that of point and line, so TPDTR improves the accuracy of hand valley point detection. The experimental results confirm the superiorities of TPDTR over the existing methods in computational cost and accuracy.

Logical Conjunction of Triple-Perpendicular-Directional Translation Residual for Contactless Palmprint Preprocessing

Limited processing power, hand pose variations, complicated backgrounds and changing illumination are some of the inherent problems in mobile palmprint recognition. In this paper, a hand-shaped guide window is proposed for fast processing of image acquisition, valley point detection and verification to deal with such problems. Also, an improved version of the Competitive Code is proposed in order to cope with the variation in mobile images, and two practical verification scenarios are suggested to further improve the verification performance. A palmprint database was established using mobile phones for experiments, and an equal error rate (EER) of 2.88% was achieved by using the conventional 1-to-1 matching strategy. By applying the suggested practical scenarios, the verification accuracy was improved to achieve an EER of 0.97%.

An empirical study of palmprint recognition for mobile phones

With the rapid development of mobile devices, smartphones have gradually become an indispensable part of people's lives. Meanwhile, biometric authentication has been corroborated to be an effective method for establishing a person's identity with high confidence. Hence, recently, biometric technologies for smartphones have also become increasingly sophisticated and popular. But it is noteworthy that the application potential of palmprints for smartphones is seriously underestimated. Studies in the past two decades have shown that palmprints have outstanding merits in uniqueness and permanence, and have high user acceptance. However, currently, studies specializing in palmprint verification for smartphones are still quite sporadic, especially when compared to face- or fingerprint-oriented ones. In this paper, aiming to fill the aforementioned research gap, we conducted a thorough study of palmprint verification on smartphones and our contributions are twofold. First, to facilitate the study of palmprint verification on smartphones, we established an annotated palmprint dataset named MPD, which was collected by multi-brand smartphones in two separate sessions with various backgrounds and illumination conditions. As the largest dataset in this field, MPD contains 16,000 palm images collected from 200 subjects. Second, we built a DCNN-based palmprint verification system named DeepMPV+ for smartphones. In DeepMPV+, two key steps, ROI extraction and ROI matching, are both formulated as learning problems and then solved naturally by modern DCNN models. The efficiency and efficacy of DeepMPV+ have been corroborated by extensive experiments. To make our results fully reproducible, the labeled dataset and the relevant source codes have been made publicly available at this https URL.

pdf/towards-palmprint-verification-on-smartphones-594k1670bm.pdf

Towards Palmprint Verification On Smartphones

With the rapid development of the mobile communication market the need for mobile biometrics emerges. This is a novel research topic within biometrics and not much work has been done in the past. This paper presents the initial work of a long-term project towards a robust mobile palmprint verification system. We develop a hand orientation normalization method which makes the palmprint acquisition on a mobile phone practical for casual use. The competitive code is adopted and accelerated for fast code matching. A performance study is performed on a dataset acquired using an iPhone. The achieved results provide a credible indication of the potential of palmprint verification in a mobile context and motivate further work.

Palmprint Verification on Mobile Phones Using Accelerated Competitive Code

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Palmprint Verification on Mobile Phones Using Accelerated Competitive Code