Department of Electrical and Electronics Engineering

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Author: search.filters.author.Tiwari, KamleshSubject: search.filters.subject.EEE

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    Scanned to Digital Face Images Matching With Siamese Network
    (IEEE, 2018) Gupta, Karunesh Kumar; Tiwari, Kamlesh
    Often in law enforcement and forensic application it is needed to match scanned facial image with a digital face image. This is because in many scenario, non-digital face images are obtained from the crime scene, news articles etc. that are needed to be identified. Non-digital face images are first scanned and then enhanced to match against the database. Challenges arrives because of poor quality of non-digital image, artifacts introduced in scanning process and high saturation etc. therefore matching becomes difficult. The methods used in literature involve specialized hand crafted pre-processing. In our paper, we propose an automated way of matching by using Siamese networks. The proposed method have been able to achieve an EER of 2.346% that is better than the current state-of-the-art.
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    Finger Knuckleprint Based Personal Authentication Using Siamese Network
    (IEEE, 2019) Gupta, Karunesh Kumar; Tiwari, Kamlesh
    Online security is a major concern today and incidents of forged identity cards and hacked passwords are common throughout the world. Therefore, there is a need for robust personal authentication mechanisms using biometrics for various access control systems. Popular biometric traits such as fingerprint have problems in rural areas, due to wearing down of fingerprint pattern from hard manual labor. This is also a problem for people who work with calcium oxide, because it is known to dissolve the upper layers of the skin due to its basicity. This paper proposes a finger-knuckle-print (FKP) based human authentication system that is immune to the above problems because the finger dorsal region is not exposed to labor surfaces. The paper uses pre-processed knuckle ROI images to train a Siamese convolutional neural network model. The proposed algorithm has been validated using open-source PolyU finger-knuckle-print database from 165 individuals, and has achieved 99.24% CRR, 0.78% EER that is better than the state-of-the-art.