Affiliations: [a] Department of Oral Pathology and Microbiology, PMS College of Dental Science and Research, Thiruvananthapuram, Kerala, India
| [b] Department of Computer Applications, MES College of Engineering, Kuttippuram, Kerala, India
| [c] Artificial Intelligence & Computer Vision Lab, Department of Computer Science, Cochin University of Science and Technology, Kochi, Kerala, India
Correspondence:
[*]
Corresponding author. Madhu S. Nair, Artificial Intelligence & Computer Vision Lab, Department of Computer Science, Cochin University of Science and Technology, Kochi-682022, Kerala, India. E-mail: madhu_s_nair2001@yahoo.com.
Abstract: The need of newer biometric traits is increasing, as the conventional biometric systems are found to be vulnerable to forging. Nowadays, tongue print is gaining importance as a biometric trait, especially in the area of forensics. Tongue is a well protected vital organ which exhibits rich structural patterns. Success of tongue print as a biometric tool depends on how well the discriminating features are extracted from it. Advancements in the field of deep neural network and availability of high-end computing environments facilitate remarkable progress in the area of image recognition. CNN follows a hierarchical learning to extract feature maps that highly characterize the training data. However, obtaining a tongue print dataset large enough to train a CNN for recognition poses a huge challenge. Alternatively, two techniques can be used to successfully employ CNN for recognition: fine-tuning pre-trained CNN models, to use as a classifier, with the new input dataset and class labels to perform tongue-print image recognition. Another effective method is to use a pre-trained CNN model as a feature extractor, to extract features from the input tongue dataset and then use a state-of-the-art classifier to perform image recognition. In this paper, we addressed three important factors regarding the deployment of tongue-print as a biometric tool. Since, a tongue-print dataset is not publicly available, our first objective to create a challenging tongue-print dataset. We then explored and evaluated different state-of-the-art CNN architectures for image recognition. These models are varied in their architecture and contain 5 million to 144 million parameters. Finally, we analyzed different approaches to use the pre-trained CNN models for the tongue-print identification task.
Keywords: Tongue print, biometric, identification, CNN, support vector machine, forensics
