# A Realistic Hand Image Composition Method for Palmprint ROI Embedding Attack
## 2 Related Works  
### Palmprint Recognition  
[PERSON] first introduced PalmCode [6] for PPR, which is characterized by four distinct steps: preprocessing, feature extraction, matching (or enrollment), and decision. Subsequent developments in PPR have predominantly focused on enhancing feature extraction, matching, and decision-making. These advancements include approaches based on hand-crafted methodologies [7; 8; 9; 10] as well as those rooted in deep learning [11; 12; 13; 14].  
A competitive coding scheme [7] has been introduced to enhance the discriminative features, selecting the highest responsive direction order as a feature from six-directional  
Figure 2: The illustration of low and high appearance consistency in ROI embedding. The second row depicts a seamless integration of carrier and ROI images.  
Gabor features. Building upon this competition scheme, the work [8] employs a Modified Finite Radon Transform (MFRAT) to replace Gabor, extracting more robust features. Another paper [9] enhances the discriminative nature of the competitive feature by incorporating an additional adjacent direction matrix. To leverage more comprehensive information, ref. [10] employs six-directional Gabor features and utilizes the average match score of these features to inform the decision-making process.  
In the realm of deep-learning-based approaches, several studies [11; 12] have integrated the competition mechanism with multi-scale learnable Gabor kernels to enhance feature extraction effectively. Furthermore, the work [13] employs a hashing method for extracting concise features, while [14] introduces a single convolution layer to extract multi-directional features. These PPR methods are dedicated to exploring discriminative features within the palmprint ROI.  
Conversely, other studies have focused on refining preprocessing methods [15; 16; 17; 18; 19; 20], directing their efforts toward devising universal and effective techniques for accurately locating palmprint ROIs. The majority of ROI localization methods are based on identifying finger valley points, exemplified by methodologies such as that described in [15], which employs Convolutional Neural Networks (CNN) for hand classification and segmentation [16], which employs an improved active shape model [17], which utilizes a hand landmark detector model, and [18], which integrates a segmentation network and two regression networks. Some methodologies directly predict the ROI bounding box, including [19], which incorporates a finger classifier and transfer learning, and [20], which employs a regression network to simultaneously detect palmprint and palm vein ROIs.  
A plethora of diverse methods for localizing ROI lead to variations in the obtained ROI outcomes. Recognition results, derived from matching and decision processes, are intricately dependent on preprocessing and feature extraction outcomes. Therefore, accurate knowledge of the ROI position is crucial to the efficacy of hand attacks. The successful execution of a hand attack appears unattainable without prior knowledge of the target preprocessing method, given the diverse nature of preprocessing techniques. Consequently, our research is grounded in the assumption that knowledge of the target preprocessing method is available.