Tongue print identification using deep CNN for forensic analysis

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.