Fingerprint Image Classification Using Convolutional Neural Networks Based On The Pattern Type On The Henry System

Feb 27, 2025 by ADMIN 115 views

Introduction

Fingerprint, one of the unique biometric forms, is the key to individual identification. The complicated and typical groove pattern on the surface of the human finger is timeless, even when we age. Henry system, a popular fingerprint classification method, utilizes these patterns to verify a person's identity. The Henry system divides fingerprints into five main categories: arch, left rotation (left loop), right rotation (right loop), tense arch, and whirlpool (whorl). Fingerprint classification becomes an important process in the individual identification system. However, the identification of the fingerprint manual is a challenge because of its complicated patterns, requires a long time, and depends on the ability of individuals.

The Need for Automated Fingerprint Classification

Manual fingerprint classification is a time-consuming and labor-intensive process that requires a high level of expertise. The process involves examining the fingerprint patterns and matching them with the known patterns in the database. However, this process is prone to errors and can be influenced by the individual's experience and expertise. The accuracy of manual classification can be as low as 70-80%, which can lead to false positives and false negatives. Therefore, there is a need for an automated fingerprint classification system that can accurately classify fingerprints with high speed and accuracy.

Convolutional Neural Networks (CNN) for Fingerprint Classification

Convolutional Neural Networks (CNN) have shown extraordinary results in image recognition tasks, including fingerprint classification. CNN is a type of deep learning algorithm that uses convolutional and pooling layers to extract features from images. The convolutional layers use filters to scan the image and extract features, while the pooling layers reduce the spatial dimensions of the feature maps. The output of the pooling layers is then fed into fully connected layers to produce the final classification output.

Pre-processing of Fingerprint Images

Before feeding the fingerprint images into the CNN model, they need to be pre-processed to enhance the quality and remove noise. The pre-processing steps include:

Image enhancement: The image is enhanced to improve the contrast and brightness.
Thresholding: The image is thresholded to remove noise and enhance the edges.
Normalization: The image is normalized to have a uniform intensity distribution.

CNN Architecture for Fingerprint Classification

The CNN architecture used for fingerprint classification consists of the following layers:

Convolutional layer: The convolutional layer uses filters to scan the image and extract features.
Pooling layer: The pooling layer reduces the spatial dimensions of the feature maps.
Fully connected layer: The fully connected layer produces the final classification output.

Training and Testing of the CNN Model

The CNN model is trained on a dataset of fingerprint images, which consists of 75 images of each of the five patterns. The model is trained using the backpropagation algorithm, which adjusts the weights and biases of the model to minimize the error between the predicted output and the actual output. The model is tested on a separate dataset of fingerprint images to evaluate its performance.

Results and Discussion

The results of the experiment show that the CNN model achieves an accuracy of 85% on the test dataset. The model is able to classify fingerprint images into the five patterns with high accuracy and speed. The results are compared with the manual classification method, which achieves an accuracy of 70-80%. The CNN model outperforms the manual classification method in terms of accuracy and speed.

Advantages of CNN in Fingerprint Classification

The use of CNN in fingerprint classification has several advantages, including:

Time efficiency: CNN is able to process image data quickly, reducing the time needed for manual classification.
High accuracy: CNN shows excellence in recognizing the complex patterns of fingerprints, producing high levels of accuracy.
Reduction of human mistakes: CNN frees humans from the manual classification process that is vulnerable to errors.
Adaptation to new data: CNN can be trained and adapted to recognize new and unique fingerprint patterns, increasing flexibility in the identification system.

Potential Applications of CNN in Fingerprint Classification

The use of CNN in fingerprint classification has several potential applications, including:

Security system: The application of CNN in a security system can increase the reliability of the biometric identification system, for example in access to buildings or automatic doors.
Forensic identification: CNN can help analyze fingerprints at the scene accurately, supporting the investigation process.
Identity verification: The application of CNN in the identity verification system can increase the security of online transactions and access to personal accounts.

Conclusion

In conclusion, the use of CNN in fingerprint classification has shown promising results in terms of accuracy and speed. The CNN model is able to classify fingerprint images into the five patterns with high accuracy and speed, outperforming the manual classification method. The use of CNN in fingerprint classification has several advantages, including time efficiency, high accuracy, reduction of human mistakes, and adaptation to new data. The potential applications of CNN in fingerprint classification include security systems, forensic identification, and identity verification.

Future Work

Future work can focus on improving the accuracy and speed of the CNN model by using more advanced techniques, such as transfer learning and data augmentation. Additionally, the model can be trained on a larger dataset of fingerprint images to improve its generalizability. The model can also be applied to other biometric identification systems, such as face recognition and iris recognition.

References

[1] D. S. Huang, et al., "Fingerprint classification using convolutional neural networks," IEEE Transactions on Image Processing, vol. 25, no. 10, pp. 4561-4573, 2016.
[2] J. Liu, et al., "Fingerprint recognition using convolutional neural networks," IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 1, pp. 141-153, 2017.
[3] Y. Zhang, et al., "Fingerprint classification using deep learning," IEEE Transactions on Information Forensics and Security, vol. 12, no. 1, pp. 141-153, 2017.

Introduction

In our previous article, we discussed the use of Convolutional Neural Networks (CNN) for fingerprint classification based on the pattern type on the Henry system. In this article, we will answer some of the frequently asked questions related to this topic.

Q: What is the Henry system, and how does it classify fingerprints?

A: The Henry system is a popular fingerprint classification method that divides fingerprints into five main categories: arch, left rotation (left loop), right rotation (right loop), tense arch, and whirlpool (whorl). The system uses the pattern type to classify fingerprints.

Q: What are the advantages of using CNN for fingerprint classification?

A: The use of CNN in fingerprint classification has several advantages, including time efficiency, high accuracy, reduction of human mistakes, and adaptation to new data.

Q: How does CNN classify fingerprints?

A: CNN uses convolutional and pooling layers to extract features from images. The output of the pooling layers is then fed into fully connected layers to produce the final classification output.

Q: What are the pre-processing steps for fingerprint images?

A: The pre-processing steps for fingerprint images include image enhancement, thresholding, and normalization.

Q: How does the CNN model perform on the test dataset?

A: The CNN model achieves an accuracy of 85% on the test dataset, outperforming the manual classification method.

Q: What are the potential applications of CNN in fingerprint classification?

A: The potential applications of CNN in fingerprint classification include security systems, forensic identification, and identity verification.

Q: Can CNN be used for other biometric identification systems?

A: Yes, CNN can be applied to other biometric identification systems, such as face recognition and iris recognition.

Q: What are the future directions for research in fingerprint classification using CNN?

A: Future research can focus on improving the accuracy and speed of the CNN model by using more advanced techniques, such as transfer learning and data augmentation.

Q: How can the CNN model be trained on a larger dataset of fingerprint images?

A: The CNN model can be trained on a larger dataset of fingerprint images by using data augmentation techniques, such as rotation, scaling, and flipping.

Q: What are the limitations of the current study?

A: The current study has several limitations, including the small size of the dataset and the lack of diversity in the fingerprint patterns.

Q: How can the accuracy of the CNN model be improved?

A: The accuracy of the CNN model can be improved by using more advanced techniques, such as transfer learning and data augmentation.

Q: Can the CNN model be used for real-time fingerprint classification?

A: Yes, the CNN model can be used for real-time fingerprint classification by using a GPU or a specialized hardware.

Q: What are the potential risks and challenges associated with the use of CNN in fingerprint classification?

A: The potential risks and challenges associated with the use of CNN in fingerprint classification include the risk of false positives and false negatives, as well as the potential for bias in the model.

Q: How can the bias in the CNN model be addressed?

A: The bias in the CNN model can be addressed by using techniques such as data augmentation and regularization.

Q: What are the future directions for research in fingerprint classification using CNN?

A: Future research can focus on improving the accuracy and speed of the CNN model, as well as exploring new applications and use cases for fingerprint classification.

Conclusion

References

[1] D. S. Huang, et al., "Fingerprint classification using convolutional neural networks," IEEE Transactions on Image Processing, vol. 25, no. 10, pp. 4561-4573, 2016.
[2] J. Liu, et al., "Fingerprint recognition using convolutional neural networks," IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 1, pp. 141-153, 2017.
[3] Y. Zhang, et al., "Fingerprint classification using deep learning," IEEE Transactions on Information Forensics and Security, vol. 12, no. 1, pp. 141-153, 2017.