Multi-size patch based collaborative representation for Palm Dorsa Vein Pattern recognition by enhanced ensemble learning with modified interactive artificial bee colony algorithm

Joardar, Sandip; Chatterjee, Amitava; Bandyopadhyay, Sanghamitra; Maulik, Ujjwal

doi:10.1016/j.engappai.2017.02.002

Cited by 12 publications

(5 citation statements)

References 22 publications

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“…which computes the difference between the predicted value h B (X (i) ) and the real value Y (i) , for all N instances. Other effectiveness measures include exponential squared loss (Joardar et al, 2017), geometric mean (Cao et al, 2013a,b;Rapakoulia et al, 2014;Vluymans et al, 2016), imbalance ratio (Vluymans et al, 2016), and confidence Singh et al, 2016), to name just a few. In general, such measures have the advantage of coping better with imbalanced class distributions than the aforementioned accuracy measure (or its dual error rate).…”

Section: Effectiveness Diversity Complexity and Efficiencymentioning

confidence: 99%

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A survey of evolutionary algorithms for supervised ensemble learning

Cagnini

Dôres

Freitas

et al. 2023

The Knowledge Engineering Review

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This paper presents a comprehensive review of evolutionary algorithms that learn an ensemble of predictive models for supervised machine learning (classification and regression). We propose a detailed four-level taxonomy of studies in this area. The first level of the taxonomy categorizes studies based on which stage of the ensemble learning process is addressed by the evolutionary algorithm: the generation of base models, model selection, or the integration of outputs. The next three levels of the taxonomy further categorize studies based on methods used to address each stage. In addition, we categorize studies according to the main types of objectives optimized by the evolutionary algorithm, the type of base learner used and the type of evolutionary algorithm used. We also discuss controversial topics, like the pros and cons of the selection stage of ensemble learning, and the need for using a diversity measure for the ensemble’s members in the fitness function. Finally, as conclusions, we summarize our findings about patterns in the frequency of use of different methods and suggest several new research directions for evolutionary ensemble learning.

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Section: Effectiveness Diversity Complexity and Efficiencymentioning

confidence: 99%

“…,Fuqiang et al (2014),Zhang et al (2014),Liu et al (2014b),Fatima et al (2013),Joardar et al (2017),Galar et al (2013),Cagnini et al (2018) Expression treesFolino et al (2016),Lacy et al (2015b,a),Ali and Majid (2015),Liu et al (2015Liu et al ( , 2014a,Tsakonas (2014),Escalante et al (2013) Genetic Fuzzy System, Tsakonas and Gabrys…”

mentioning

confidence: 99%

A survey of evolutionary algorithms for supervised ensemble learning

Cagnini

Dôres

Freitas

et al. 2023

The Knowledge Engineering Review

View full text Add to dashboard Cite

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“…Ensemble learning aims at combining multiple learners to obtain a more robust representation of the object and is successfully applied for vision tasks such as SAR image category (Zhao et al, 2016 ), fault diagnosis (Liu et al, 2021 ), image cluster (Tsai et al, 2014 ), and human activity recognition (Jethanandani et al, 2020 ). In addition, some researchers applied it to biometrics, e.g., classification tasks such as fingerprint classification (Zhang et al, 2011b ), palm-vein recognition (Joardar et al, 2017 ), and face recognition (Bhatt et al, 2014 ; Ding and Tao, 2018 ). As the features from different learners can achieve a complementary representation for the input image, their combination performs well for identification.…”

Section: Introductionmentioning

confidence: 99%

A deep ensemble learning method for single finger-vein identification

Liu

Qin²,

Song³

et al. 2023

Front. Neurorobot.

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Finger-vein biometrics has been extensively investigated for personal verification. Single sample per person (SSPP) finger-vein recognition is one of the open issues in finger-vein recognition. Despite recent advances in deep neural networks for finger-vein recognition, current approaches depend on a large number of training data. However, they lack the robustness of extracting robust and discriminative finger-vein features from a single training image sample. A deep ensemble learning method is proposed to solve the SSPP finger-vein recognition in this article. In the proposed method, multiple feature maps were generated from an input finger-vein image, based on various independent deep learning-based classifiers. A shared learning scheme is investigated among classifiers to improve their feature representation captivity. The learning speed of weak classifiers is also adjusted to achieve the simultaneously best performance. A deep learning model is proposed by an ensemble of all these adjusted classifiers. The proposed method is tested with two public finger vein databases. The result shows that the proposed approach has a distinct advantage over all the other tested popular solutions for the SSPP problem.

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“…Matching scores are computed and authentication is performed with an equal error rate of 1.14%. As suggested by previous studies [19], [7], image processing on single palm image often is not enough for user authentication. Fusion techniques and supervised learning are used on multiple sample images for the same user for efficient recognition.…”

Section: Introductionmentioning

confidence: 99%

“…The image fusion technique brings up significant improvements in terms of user recognition performance. A supervised learning algorithm based on interactive artificial bee colony is used in [7] for collaborative representation of palm vein pattern using multiple images for the same user. Personal authentication is also successfully achieved using vein triangulation and knuckle shape in [8].…”

Section: Introductionmentioning

confidence: 99%

Dorsal Hand Vein Pattern Analysis and Neural Networks for Biometric Authentication

Belean¹,

Streza²,

Crisan³

et al. 2017

STUD INFORM CONTROL

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Over the past several years, the subcutaneous blood vessels have emerged as a new solution for identity management. Biometric systems based on hand veins are considered to be very promising for high security environments. In this paper, we propose a novel user authentication approach based on dorsal hand vein pattern analysis and multi-layer perceptron neural network classification. For image processing two different techniques are employed: rotation invariant Hough transform and clustering based segmentation and mathematic morphology. Both approaches lead to binary images containing the vein patterns. The vessel structure corresponding to hand image samples of the same user are used to extract the final features, independent of hand rotation and distance to the camera lens during acquisition. These characteristics are used to train the neural network, whereas are computed for the new input images to be classified as corresponding to one of the legitimate subjects or not. The experimental analysis shows that user classification with an equal error rate of 0.83% can be attained, bringing the advantages of proposed image processing techniques for vein detection and neural network classification into complete synergy.

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Multi-size patch based collaborative representation for Palm Dorsa Vein Pattern recognition by enhanced ensemble learning with modified interactive artificial bee colony algorithm

Cited by 12 publications

References 22 publications

A survey of evolutionary algorithms for supervised ensemble learning

A survey of evolutionary algorithms for supervised ensemble learning

A deep ensemble learning method for single finger-vein identification

Dorsal Hand Vein Pattern Analysis and Neural Networks for Biometric Authentication

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