## 2 Related Work  
Signature verification techniques can be classified into two approaches: function-based and feature-based [20]. Function-based signature verification refers to the matching process using the original time-series data of a signature. The feature extraction process could be absent. Well-known function-based approaches include the dynamic time warping (DTW) algorithm [21], root mean square method (RMS) [22], and hidden Markov model (HMM) [23; 24]. Generally, function-based systems demonstrate better verification performance than feature-based systems [24]. However, during the matching process, a dynamic construction of the original signature is revealed, resulting in a potential privacy breach if the matching must be done remotely [11]. Furthermore, the system is more complex and requires longer execution time than feature-based systems [4].  
Feature-based signature verification refers to the process of statistical similarity comparison using descriptive features of a signature. A signature is accepted as being genuine if the similarity score exceeds a certain threshold. Feature-based systems are lightweight, less complex, and faster than function-based systems [4]; thus, they are suitable for mobile environments. The main issues of feature-based systems pertain to the extraction of effective features and application of efficient verification algorithms. [PERSON] et al. [5] proposed a set of diverse features such as total duration of the signature, pen pressure, and the number of times the pen is lifted.  
Based on this feature set, [PERSON] [6] proposed a multimatcher method to verify signatures. In addition, [PERSON] and [PERSON] [7] introduced a symbolic representation of an online signature.  
To improve the accuracy of signature verification, some studies [8; 9; 10] utilize machine learning techniques, which are one of the most noteworthy technologies. Similar to the process in feature-based signature verification, they use descriptive features of signatures for building a subject model. [PERSON] et al. [9] exploited MLP for signature verification, and demonstrated an average accuracy of 82.42% for recognizing the subject; however, this method could not distinguish between imitated and original signatures. As mentioned previously, [PERSON] et al. [10] used AE, the one-class model, to distinguish imitated signatures. They derived the \((x,y)\) coordinates, sign changes \((dx/dt)\), \((dy/dt)\), pen pressure, and pen angle as features. The verification accuracy was 92% for classifying subject and imitated signatures.  
However, all of the above-mentioned feature-based signature verification schemes focus on verifying signatures drawn by stylus instead of finger drawn signature. As mentioned above, verification of finger drawn signatures currently receives great attention but is still a challenging issue due to the lack of precise signals and features. [PERSON] et al. [11], [PERSON] et al. [12], and [PERSON] et al. [13] propose their own matching algorithms, not the machine learning techniques, for finger drawn signature verification. [PERSON] [3] applies deep learning algorithm for finger drawn signature verification using dynamic features such as finger movements and phone movements. However, they exploit MLP, two-class classifier similar to [PERSON] et al. [9], and suffer from the same problem, i.e., trouble of distinguishing imitated signatures.  
This work focuses on the finger-drawn signature verification scheme using deep learning. To efficiently distinguish the imitated signatures, the proposed method exploits AE as the one-class model. As mentioned above, finger-drawn signature verification has limitation owing to less precise signals and fewer features. The proposed method therefore uses CNN for feature extraction to improve the verification accuracy.