**2. Related Works**

Different works of the literature pointed out the diversity of acquisition conditions and the subsequent degradation of verification performance in the mobile context [2,15,16,26–39]. The majority of these works focused on assessing and improving verification performance in several ways.

Martinez-Diaz et al. [26] indicated that ASV systems traditionally used on signatures acquired with Wacom digitizing tablets in an office-like scenario should be adapted in the context of handheld touch-screen devices. Indeed, the Wacom digitizing tablet used needs to be connected to a personal computer and thus leads to an office-like scenario. The authors exploited the BioSecure datasets DS2 and DS3 that contain signatures of the same 120 persons acquired, respectively, on a Wacom digitizing tablet following an office-like scenario and on a mobile touch-screen sensor (PDA) while holding it in the hand. Based on a feature selection algorithm and a hidden markov model (HMM) classifier, they observed the low discriminative power of dynamic features and the high consistency of geometric features in the mobile scenario.

Houmani et al. [27] evaluated an HMM-based classifier on two different databases both acquired on a PDA, namely PDA-64 containing online signatures of 64 persons, and BioSecure DS3 dataset (DS3-210) containing online signatures of 210 persons. Experiments showed significant performance degradation in mobile conditions: an average Equal Error Rate (EER) of 3.5% is obtained on the Wacom digitizer with skilled forgeries, while on PDA-64 and DS3-210, the EER is of 16.02% and 9.95% respectively.

In the context of the international online signature competition BSEC'2009 [15], different ASV systems were assessed on two large BioSecure datasets containing signatures of the same 382 persons acquired in a controlled scenario on a Wacom digitizer, namely DS2-382, and on a mobile device (PDA), namely DS3-382. Results showed a clear degradation of systems' performance when signing in mobile conditions.

Another work on BioSecure databases was conducted by Houmani and Garcia-Salicetti [28] to quantify the quality of signatures of the same 104 persons when captured in office-like conditions (DS2-104, captured on a Wacom digitizer) and in the mobile context (DS3-104, captured on a PDA). Results showed that signatures' quality degrades in mobile conditions and especially signature complexity decreases.

Blanco-Gonzalo et al. [29] evaluated an ASV system based on dynamic time warping (DTW), on seven mobile devices (using stylus and finger): two Wacom tablets, a tactile laptop, a Samsung Galaxy Note, an iPad, a Samsung Galaxy Tab and a Blackberry Playbook. They confronted the system with different acquisition conditions, such as sensor technology, interoperability, visual feedback, and screen size. Experiments showed that the best system performance is obtained when signing on a small screen, using a stylus instead of the finger. However, this study is of limited scope because only 11 writers were considered. In [31], Blanco-Gonzalo et al. exploited a DTW-based classifier on a database containing signatures of 43 users acquired on different mobile platforms. However, performance assessment was carried out only on random forgeries.

Martinez-Diaz et al. [32] presented the first publicly available database collected on a touch-screen sensor embedded in a mobile phone, namely the DooDB database. The database contains finger-drawn doodles and pseudo-signatures from 100 persons and skilled forgeries for all of them. To create pseudo-signatures, participants were asked to draw a simplified version of their signature, for example signing with their initials or part of their signature flourish, which could be used as a graphical password. Using a DTW-based classifier, the authors obtained an EER of around 26.9% on skilled forgeries considering time variability and 19.8% when the system was evaluated on only one session.

Sae-Bae and Memon [34] collected a new database that contains finger-drawn signatures from 180 persons captured in uncontrolled mobile conditions, on user owned iOS mobile devices. The authors proposed a histogram-based feature set for representing an online signature. They pointed out the importance of updating reference signatures to reduce the intra-class variability and thus improve systems' performance. The authors claimed that personalized feature selection is necessary to attain an acceptable performance level; they obtained an EER of 3.18% on random forgeries.

Antal et al. [36] introduced the MOBISIG database that contains finger-drawn pseudo-signatures from 83 persons, captured on a capacitive touch-screen sensor embedded in a mobile device. Participants were asked to create a signature for a given family name and were instructed on how to produce signatures with their finger. For performance assessment, the authors used a personalized threshold; they obtained an EER of 8.56% with a DTW classifier (vs. 25.45% with a global threshold), considering skilled forgeries and five reference signatures in the enrollment phase. Then, when considering 15

samples for enrollment, they improved verification performance, reaching an EER of 5.81% with a DTW classifier and a personalized threshold (vs. 20.82% with a global threshold).

Tolosana et al. [37] showed that performance is much better with the stylus than with the finger, on 65 persons of the e-BioSign database, using signatures captured with both Wacom tablets and Samsung mobile devices. Based on a DTW classifier, the authors obtained an EER of 22.1% with the finger versus roughly 7.9% with the stylus, considering skilled forgeries. This performance is obtained on 35 persons of the evaluation dataset; the 30 remaining persons were used as a development dataset to select 23 relevant parameters among the whole set of 117 features.

Zareen and Jabin [38] used a publicly available database [33] that contains 500 signatures from 25 persons, acquired on a Samsung Galaxy Note. As no skilled forgeries were acquired, the verification system based on a feed-forward multilayer neural network was evaluated only on random forgeries. The authors obtained an EER of 0.12% on random forgeries. These results seem preliminary since only 25 persons were considered and no skilled forgeries were used for performance assessment.

Nam et al. [39] used a private database that contains real finger-drawn signatures of only 20 persons, collected on a Samsung Galaxy S3. The authors proposed convolutional neural networks (CNN) for feature extraction, trained with genuine and forged signatures. Then, using an autoencoder for classification, they obtained an EER of 4.4% on skilled forgeries. However, this study is of limited scope because only 20 writers were considered.

All the above-mentioned works pointed out the degradation of systems' performance in the mobile context. However, in most of them, the data corpora presented do not contain signatures acquired in totally uncontrolled mobile conditions. This specific point is sometimes not even mentioned in the description of the acquisition protocol used, mainly focused on the sensor characteristics (technology, resolution, sampling rate), the writing tool, the design of the interface for acquisition, and the number of captured genuine signatures and forgeries. In addition, some works [32,36] evaluated the impact of mobile conditions based on pseudo-signatures, which are not exploited in real-world usages. Other studies evaluated ASV systems only on random forgeries because of the burden of acquiring skilled forgeries [34,38]. Finally, some works considered new challenging scenarios for ASV system assessment in terms of interoperability, as enrolling the person with a given writing tool and testing the system with another one [37].

In conclusion, we notice significant efforts in the literature for assessing verification performance in mobile conditions with different sensors, scenarios and classification strategies. Most works focused on the development of algorithms for biometric verification to enhance user authentication: DTW, HMM, neural networks and more recently some deep architectures. However, none of these works addressed quality-driven signature verification since the enrollment step, by quantifying and enhancing the information content of input data given to the sensor.
