A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification

Xiao, Wanghui; Ding, Yuting

doi:10.3390/sym14061216

Cited by 14 publications

(7 citation statements)

References 41 publications

(56 reference statements)

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“…The handwriting discrimination method proposed in this paper presents higher classification success compared to the methods given in [14,16], as well as almost all the other methods presented in the literature given in the Introduction.…”

Section: Classification Performance Comparisonmentioning

confidence: 77%

“…This survey examines symmetrical neural network architectures-Siamese and triplet. Offline handwritten signature verification is one of the most prevalent and prominent biometric methods in many application fields [16]. This study proposes a twostage Siamese neural network model for accurate offline handwritten signature verification with two main ideas: (a) adopting a two-stage Siamese neural network to verify original and enhanced handwritten signatures simultaneously and (b) utilizing the focal loss to deal with the extreme imbalance between positive and negative offline signatures.…”

Section: Introductionmentioning

confidence: 99%

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Short Words for Writer Identification Using Neural Networks

Κούκιου

2023

Applied Sciences

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In biometrics, it is desirable to distinguish a person using only a short sample of his handwriting. This problem is treated in the present work using only a short word with three letters. It is shown that short words can contribute to high-performance writer identification if line characteristics are extracted using morphological directional transformations. Thus, directional morphological structuring elements are used as a tool for extracting this kind of information with the morphological opening operation. The line characteristics are organized based on Markov chains so that the elements of the transition matrix are used as feature vectors for identification. The Markov chains describe the alternation in the directional line features along the word. The analysis of the feature space is carried out using the Fisher linear discriminant method. The identification performance is assessed using neural networks, where the simplest neural structures are sought. The capabilities of these simple neural structures are investigated theoretically concerning the achieved separability into the feature space. The identification capabilities of the neural networks are further assessed using the leave-one-out method. It is proved that the neural methods achieve identification performance that approaches 100%. The significance of the proposed method is that it is the only one in the literature that presents high identification performance using only one short word. Furthermore, the features used as well as the classifiers are simple and robust. The method is independent of the language used regardless of the direction of writing. The NIST database is used for extracting short-length words having only three letters each.

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Section: Classification Performance Comparisonmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Short Words for Writer Identification Using Neural Networks

Κούκιου

2023

Applied Sciences

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“…In the training process, these two types of networks use two different loss functions: a contrastive loss function in the twin network architecture, and a triplet loss function in the triplet architecture. Applications of Siamese networks include object tracking [ 32 ], face recognition [ 33 ], and signature verification [ 34 ]. In the hyperspectral image domain, research can be found in the application areas of image classification [ 35 ] and object/target detection [ 36 , 37 ].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Feature Detection and Matching in Low-Pixel-Resolution Hyperspectral Images Using 3D Convolution-Based Siamese Networks

Perera,

Premachandra,

Kawanaka

2023

Sensors

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Today, hyperspectral imaging plays an integral part in the remote sensing and precision agriculture field. Identifying the matching key points between hyperspectral images is an important step in tasks such as image registration, localization, object recognition, and object tracking. Low-pixel resolution hyperspectral imaging is a recent introduction to the field, bringing benefits such as lower cost and form factor compared to traditional systems. However, the use of limited pixel resolution challenges even state-of-the-art feature detection and matching methods, leading to difficulties in generating robust feature matches for images with repeated textures, low textures, low sharpness, and low contrast. Moreover, the use of narrower optics in these cameras adds to the challenges during the feature-matching stage, particularly for images captured during low-altitude flight missions. In order to enhance the robustness of feature detection and matching in low pixel resolution images, in this study we propose a novel approach utilizing 3D Convolution-based Siamese networks. Compared to state-of-the-art methods, this approach takes advantage of all the spectral information available in hyperspectral imaging in order to filter out incorrect matches and produce a robust set of matches. The proposed method initially generates feature matches through a combination of Phase Stretch Transformation-based edge detection and SIFT features. Subsequently, a 3D Convolution-based Siamese network is utilized to filter out inaccurate matches, producing a highly accurate set of feature matches. Evaluation of the proposed method demonstrates its superiority over state-of-the-art approaches in cases where they fail to produce feature matches. Additionally, it competes effectively with the other evaluated methods when generating feature matches in low-pixel resolution hyperspectral images. This research contributes to the advancement of low pixel resolution hyperspectral imaging techniques, and we believe it can specifically aid in mosaic generation of low pixel resolution hyperspectral images.

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“…The distance measure in new space is then adopted to evaluate the similarity of two inputs. Siamese network has been successfully adopted in many realms, such as semantic similarity analysis [24], handwriting font recognition [25], and visual tracking algorithm [26]. Therefore, it is of great significance in many mission-critical applications.…”

Section: Siamese Networkmentioning

confidence: 99%

Transmission Defects Localization Network: Towards Wrong Assembly in the Transmission Assembly Process

2023

ACSS

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Transmission valve component is an important part of automobile manufacture. The success of the assembly of transmission valve is directly related to driving safety of vehicles. While localizing transmission assembly defects is particularly important in assembly of transmission valve component. As an image processing problem, real-time assembly images of transmission valve component are adopted to determine whether the assembly is correct or wrong. Transmission valve component in these images have small, severe reflection, and sparse properties, which increases the difficulty of detection. Therefore, this paper proposes a transmission defects localization network based on Siamese network for improving the performance of assembly of transmission valve components. In our model, we establish an image similarity evaluation network with designed multi-scale features fusion approach. Furthermore, in order to reduce intra-class spacing by similar transmission valve part samples on evaluation action, an improved binary cross entropy and focal loss function is discovered for feature re-processing. Finally, experimental results on real-world transmission assembly dataset indicate that our proposed approach outperforms other compared methods.

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A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification

Cited by 14 publications

References 41 publications

Short Words for Writer Identification Using Neural Networks

Short Words for Writer Identification Using Neural Networks

Enhancing Feature Detection and Matching in Low-Pixel-Resolution Hyperspectral Images Using 3D Convolution-Based Siamese Networks

Transmission Defects Localization Network: Towards Wrong Assembly in the Transmission Assembly Process

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