This paper presents a comprehensive survey examining the prevailing feature extraction methodologies employed within biometric palmprint recognition models. It encompasses a critical analysis of extant datasets and a comparative study of algorithmic approaches. Specifically, this review delves into palmprint recognition systems, focusing on different feature extraction methodologies. As the dataset wields a profound impact within palmprint recognition, our study meticulously describes 20 extensively employed and recognized palmprint datasets. Furthermore, we classify these datasets into two distinct classes: contact-based datasets and contactless-based datasets. Additionally, we propose a novel taxonomy to categorize palmprint recognition feature extraction approaches into line-based approaches, texture descriptor-based approaches, subspace learning-based methods, local direction encoding-based approaches, and deep learning-based architecture approaches. Within each class, most foundational publications are reviewed, highlighting their core contributions, the datasets utilized, efficiency assessment metrics, and the best outcomes achieved. Finally, open challenges and emerging trends that deserve further attention are elucidated to push progress in future research. 

Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions

Palmprint recognition has been widely applied to security authentication due to its rich characteristics, i.e., local direction, wrinkle, and texture. However, different types of palmprint images captured from different application scenarios usually contain a variety of dominant features. Specifically, the palmprint recognition performance will be degraded by the interference factors, i.e., noise, rotations, and shadows, while palmprint images are acquired in the open-set environments. Seeking to handle the long-standing interference information in the images, multiview palmprint feature learning has been proposed to enhance the feature expression by exploiting multiple characteristics from diverse views. In this paper, we first introduced six types of palmprint representation methods published from 2004 to 2022, which described the characteristics of palmprints from a single view. Afterward, a number of multiview-learning-based palmprint recognition methods (2004–2022) were listed, which discussed how to achieve better recognition performances by adopting different complementary types of features from multiple views. To date, there is no work to summarize the multiview fusion for different types of palmprint features. In this paper, the aims, frameworks, and related methods of multiview palmprint representation will be summarized in detail.

https://www.mdpi.com/2227-7390/11/5/1261/pdf?version=1678085749

Multiview-Learning-Based Generic Palmprint Recognition: A Literature Review

Palmprint-based biometric recognition has received tremendous attention due to its several advantages such as high security, non-invasive and good hygiene propensities. Recent deep convolutional neural network (CNN) has been successfully applied for palmprint recognition and achieved promising performance due to its breakthroughs in image classification, which however usually requires a massive amount of labeled samples to finetune the network. In this paper, we propose a compact CNN with limited layers for palmprint recognition by embedding double attention mechanisms into the convolutional layers. Specifically, we first design a channel attention module to learn and select the discriminative channel maps by adaptively optimizing the attention weights among all channels. Then, we engineer a location attention module to learn the position-specific features of the palmprints. Both the channel and location attention modules are subsequently embedded into each convolutional layer, such that more discriminative features can be efficiently exploited during feature learning. Lastly, we train a fully convolutional network as the classifier for feature identification. Extensive experimental results on three widely used databases demonstrate the effectiveness of the proposed method in comparison with the state-of-the-arts.

Compact Double Attention Module Embedded CNN for Palmprint Recognition

Biometrics, as an identification method, is used for various applications, particularly in security technologies. The integration of multiple biometric sources aims to overcome limitations observed in unimodal systems, enhancing recognition accuracy. Fusion techniques, categorized into sensor level, feature level, matching score level, decision level, and rank level, are explored to optimize the combination of information from different modalities. Various fusion schemes, such as feature-level fusion, decision-level fusion, and hybrid systems, are investigated for their effectiveness in integrating diverse biometric traits. This paper details the fusion schemes at different levels, including sensor, feature extraction, matching score, and decision levels. Experimental results demonstrate the efficacy of the proposed multimodal biometric system. The Equal Error Rate (EER) is analyzed to evaluate system accuracy, with weights assigned to each modality based on their performance. Normalization techniques and fusion rules are applied to combine modalities, resulting in enhanced matching scores. The analysis of results showcases the performance of the system across various fusion combinations. Notably, the combination of ear and foot modalities yields the highest matching score, demonstrating the effectiveness of fusion techniques in multimodal biometrics.

Multimodal biometrics system with face, ear, and foot fusion techniques

Orientation based coding method is one of the most important approaches in palmprint recognition, which achieves impressive performance by extracting one or more dominant orientation features, and calculating the distance between features of two palmprints. However, simply using the orientation features may be vulnerable to noisy and rotation. In this paper, we proposed a novel orientation-space code scheme to represent the orientation space feature of palmprint and designed a novel multi-feature two-phase sparse representation (MTPSR) scheme for feature matching. Extensive experiments on three benchmark palmprint databases are conducted to demonstrate the high effectiveness of the proposed method

Orientation space code and multi-feature two-phase sparse representation for palmprint recognition

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Orientation space code and multi-feature two-phase sparse representation for palmprint recognition