LDA-CNN: Linear Discriminant Analysis Convolution Neural Network for Periocular Recognition in the Wild
Due to the COVID-19 pandemic, the necessity for a contactless biometric system able to recognize masked faces drew attention to the periocular region as a valuable biometric trait. However, periocular recognition remains challenging for deployments in the wild or in unconstrained environments where...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/23/4604 |
| _version_ | 1797462724688478208 |
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| author | Amani Alahmadi Muhammad Hussain Hatim Aboalsamh |
| author_facet | Amani Alahmadi Muhammad Hussain Hatim Aboalsamh |
| author_sort | Amani Alahmadi |
| collection | DOAJ |
| description | Due to the COVID-19 pandemic, the necessity for a contactless biometric system able to recognize masked faces drew attention to the periocular region as a valuable biometric trait. However, periocular recognition remains challenging for deployments in the wild or in unconstrained environments where images are captured under non-ideal conditions with large variations in illumination, occlusion, pose, and resolution. These variations increase within-class variability and between-class similarity, which degrades the discriminative power of the features extracted from the periocular trait. Despite the remarkable success of convolutional neural network (CNN) training, CNN requires a huge volume of data, which is not available for periocular recognition. In addition, the focus is on reducing the loss between the actual class and the predicted class but not on learning the discriminative features. To address these problems, in this paper we used a pre-trained CNN model as a backbone and introduced an effective deep CNN periocular recognition model, called linear discriminant analysis CNN (LDA-CNN), where an LDA layer was incorporated after the last convolution layer of the backbone model. The LDA layer enforced the model to learn features so that the within-class variation was small, and the between-class separation was large. Finally, a new fully connected (FC) layer with softmax activation was added after the LDA layer, and it was fine-tuned in an end-to-end manner. Our proposed model was extensively evaluated using the following four benchmark unconstrained periocular datasets: UFPR, UBIRIS.v2, VISOB, and UBIPr. The experimental results indicated that LDA-CNN outperformed the state-of-the-art methods for periocular recognition in unconstrained environments. To interpret the performance, we visualized the discriminative power of the features extracted from different layers of the LDA-CNN model using the t-distributed Stochastic Neighboring Embedding (t-SNE) visualization technique. Moreover, we conducted cross-condition experiments (cross-light, cross-sensor, cross-eye, cross-pose, and cross-database) that proved the ability of the proposed model to generalize well to different unconstrained conditions. |
| first_indexed | 2024-03-09T17:40:44Z |
| format | Article |
| id | doaj.art-709f28b044234d33b46edf3634095d65 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T17:40:44Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-709f28b044234d33b46edf3634095d652023-11-24T11:36:29ZengMDPI AGMathematics2227-73902022-12-011023460410.3390/math10234604LDA-CNN: Linear Discriminant Analysis Convolution Neural Network for Periocular Recognition in the WildAmani Alahmadi0Muhammad Hussain1Hatim Aboalsamh2Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaDue to the COVID-19 pandemic, the necessity for a contactless biometric system able to recognize masked faces drew attention to the periocular region as a valuable biometric trait. However, periocular recognition remains challenging for deployments in the wild or in unconstrained environments where images are captured under non-ideal conditions with large variations in illumination, occlusion, pose, and resolution. These variations increase within-class variability and between-class similarity, which degrades the discriminative power of the features extracted from the periocular trait. Despite the remarkable success of convolutional neural network (CNN) training, CNN requires a huge volume of data, which is not available for periocular recognition. In addition, the focus is on reducing the loss between the actual class and the predicted class but not on learning the discriminative features. To address these problems, in this paper we used a pre-trained CNN model as a backbone and introduced an effective deep CNN periocular recognition model, called linear discriminant analysis CNN (LDA-CNN), where an LDA layer was incorporated after the last convolution layer of the backbone model. The LDA layer enforced the model to learn features so that the within-class variation was small, and the between-class separation was large. Finally, a new fully connected (FC) layer with softmax activation was added after the LDA layer, and it was fine-tuned in an end-to-end manner. Our proposed model was extensively evaluated using the following four benchmark unconstrained periocular datasets: UFPR, UBIRIS.v2, VISOB, and UBIPr. The experimental results indicated that LDA-CNN outperformed the state-of-the-art methods for periocular recognition in unconstrained environments. To interpret the performance, we visualized the discriminative power of the features extracted from different layers of the LDA-CNN model using the t-distributed Stochastic Neighboring Embedding (t-SNE) visualization technique. Moreover, we conducted cross-condition experiments (cross-light, cross-sensor, cross-eye, cross-pose, and cross-database) that proved the ability of the proposed model to generalize well to different unconstrained conditions.https://www.mdpi.com/2227-7390/10/23/4604periocular biometricmobile biometricsdeep learningconvolutional neural networktransfer learningfine-tuning |
| spellingShingle | Amani Alahmadi Muhammad Hussain Hatim Aboalsamh LDA-CNN: Linear Discriminant Analysis Convolution Neural Network for Periocular Recognition in the Wild Mathematics periocular biometric mobile biometrics deep learning convolutional neural network transfer learning fine-tuning |
| title | LDA-CNN: Linear Discriminant Analysis Convolution Neural Network for Periocular Recognition in the Wild |
| title_full | LDA-CNN: Linear Discriminant Analysis Convolution Neural Network for Periocular Recognition in the Wild |
| title_fullStr | LDA-CNN: Linear Discriminant Analysis Convolution Neural Network for Periocular Recognition in the Wild |
| title_full_unstemmed | LDA-CNN: Linear Discriminant Analysis Convolution Neural Network for Periocular Recognition in the Wild |
| title_short | LDA-CNN: Linear Discriminant Analysis Convolution Neural Network for Periocular Recognition in the Wild |
| title_sort | lda cnn linear discriminant analysis convolution neural network for periocular recognition in the wild |
| topic | periocular biometric mobile biometrics deep learning convolutional neural network transfer learning fine-tuning |
| url | https://www.mdpi.com/2227-7390/10/23/4604 |
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