Tongue print identification using deep CNN for forensic analysis

Sadasivan, Santhu; Sivakumar, Trivandrum T.; Joseph, Anna P.; Zacharias, Geevar C.; Nair, Madhu S.

doi:10.3233/jifs-179722

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…They aimed to create a 3D representation of the tongue that described the texture and form of the tongue images. They constructed a database of 3D tongue images that defined the images' texture and form [3]. Every day, all money transactions and payments are made through the internet.…”

Section: Introductionmentioning

confidence: 99%

People identification via tongue print using fine-tuning deep learning

Obaid,

Y. Kamil,

Hamza

2023

IJRES

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<span>Many person-verification systems are critical in security systems for verifying passage through doors opened to specific people using various techniques. People can use electronic payment methods and security apps to generate codes for quick, remote financial transactions. Older systems required precision and speed. Many alternative methods were developed by technology and artificial intelligence to make such operations simple and quick. The identification of tongue prints is discussed in this paper. Tongue prints, like fingerprints, are unique to each individual. The tongue was used in this study because it is unique among such organs. The tongue is protected by the lips. This guards against taking a tongue print by force. Some people distort their fingerprints, making fingerprint recognition systems unable to recognize them. Car accidents cause facial distortion, which distorts the system and prevents it from distinguishing facial prints, so the tongue was used as a fingerprint in this study. A database of 1,104 images for 138 Mustansiriyah University College of Science students yielded an average of eight images per individual. VGG16 was implemented for transfer learning and fine-tuning. In comparison to previous studies, the accuracy achieved was more than 91%.</span>

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Section: Introductionmentioning

confidence: 99%

People identification via tongue print using fine-tuning deep learning

Obaid,

Y. Kamil,

Hamza

2023

IJRES

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“…They aimed to create a 3D representation of the tongue that described the texture and form of the tongue images. They constructed a database of 3D tongue images that defined the images' texture and form [8]. Every day, all money transactions and payments are made through the Internet.…”

Section: Introductionmentioning

confidence: 99%

People Recognition via Tongue Print Using Deep and Machine Learning

Kamil

Hamza

2023

JAIT

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The tongue is a unique organ that is well protected inside the mouth and not affected by external factors; it is also difficult to forge. Several biometric systems are widely used for authentication and recognition, such as fingerprints, faces, iris, sound, retina, etc. Traditional biometrics represent a challenge and an obstacle as they can be falsified, duplicates can be made (e.g., iris, face, fingers, signature), or they are expensive and rarely used (e.g., DNA). The increased security measures called for modern biometrics that is more secure, less expensive, and cannot be falsified. As a result, the goal of this paper is to create a system for distinguishing people based on their tongue prints. It will contribute to solving many forensic issues and increasing electronic security because it has features suitable for identification and biometrically distinguishing between people. In this paper, the tongue is located based on the fixed window size method. After tongue localization (ROI), feature extraction using the VGG-16 model, and a classification system that uses both transfer learning and machine learning as VGG-16, XGBoost, KNN, and RF classifiers, extracted features are then trained for personal identification. The dataset consisted of 1085 tongue images of 138 people with a test ratio of 20%, and the results achieved an accuracy of 92%. The process of distinguishing people through tongue prints has proven to be effective and accurate.

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“…Convolutional neural networks are also widely applied in speech recognition (Partha et al, 2020 ; Pradeep and Nirmaladevi, 2021 ; Yang et al, 2021 ), medical diagnosis (Seo and Kim, 2020 ; Toktam et al, 2020 ; Mustaqeem, 2021 ), biometrics (Alay, 2020 ; Sadasivan et al, 2020 ; Mekruksavanich and Jitpattanakul, 2021 ; Mohaghegh and Payne, 2021 ), and other fields. The exploration of convolutional neural networks in other fields provides a reference for us to use CNN for ship image recognition (Cazzato et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Recognition and Classification of Ship Images Based on SMS-PCNN Model

Huang

Zhang

et al. 2022

Front. Neurorobot.

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In the field of ship image recognition and classification, traditional algorithms lack attention to the differences between the grain of ship images. The differences in the hull structure of different categories of ships are reflected in the coarse-grain, whereas the differences in the ship equipment and superstructures of different ships of the same category are reflected in the fine-grain. To extract the ship features of different scales, the multi-scale paralleling CNN oriented on ships images (SMS-PCNN) model is proposed in this paper. This model has three characteristics. (1) Extracting image features of different sizes by parallelizing convolutional branches with different receptive fields. (2) The number of channels of the model is adjusted two times to extract features and eliminate redundant information. (3) The residual connection network is used to extend the network depth and mitigate the gradient disappearance. In this paper, we collected open-source images on the Internet to form an experimental dataset and conduct performance tests. The results show that the SMS-PCNN model proposed in this paper achieves 84.79% accuracy on the dataset, which is better than the existing four state-of-the-art approaches. By the ablation experiments, the effectiveness of the optimization tricks used in the model is verified.

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Tongue print identification using deep CNN for forensic analysis

Cited by 6 publications

References 10 publications

People identification via tongue print using fine-tuning deep learning

People identification via tongue print using fine-tuning deep learning

People Recognition via Tongue Print Using Deep and Machine Learning

Recognition and Classification of Ship Images Based on SMS-PCNN Model

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