TY - GEN
T1 - Siamese Network and Facial Ratios for Deformed Facial Matching
AU - Sharma, Ananya
AU - Prabhu, Srikanth
AU - Yadav, Aryamaan
AU - Prithviraj, P.
AU - Sigatapu, Vikas Venkat
AU - Kumar, Pramod
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Facial Recognition is a technique that uses the face and its features to identify/verify a person. It is a Biometric Application, which is playing an integral role in today’s world in a wide variety of areas like Criminal Identification, Visitor Verification, and many other Real Time Identification systems. In this paper, we present a system that does facial matching for deformed faces. The proposed method combines the extraction of high-level feature representations using Deep Learning and the calculation of facial ratios using Image Processing to generate a robust model that can be used to identify/verify deformed faces. Whereas traditional face recognition systems show poor results when there is a face deformation. We use a function that tells us how similar or how different the two input images are. So, for this Siamese network is used. This network takes in an image as input and gives its feature vector. Then, we find the distance between the computed feature vectors of the two images which will give us a similarity score. Next, we use an Image Processing technique to calculate the facial ratios from macro features like eyes, lips, etc. These facial ratios are calculated based on facial landmarks spread across the face. We use multiple facial ratios, so even if deformities occur in a part of the face it will not have much effect on the system rendering it immune to facial changes. Hence, this paper tries to close the gap in the previously devised facial matching methods which were not designed for deformed faces.
AB - Facial Recognition is a technique that uses the face and its features to identify/verify a person. It is a Biometric Application, which is playing an integral role in today’s world in a wide variety of areas like Criminal Identification, Visitor Verification, and many other Real Time Identification systems. In this paper, we present a system that does facial matching for deformed faces. The proposed method combines the extraction of high-level feature representations using Deep Learning and the calculation of facial ratios using Image Processing to generate a robust model that can be used to identify/verify deformed faces. Whereas traditional face recognition systems show poor results when there is a face deformation. We use a function that tells us how similar or how different the two input images are. So, for this Siamese network is used. This network takes in an image as input and gives its feature vector. Then, we find the distance between the computed feature vectors of the two images which will give us a similarity score. Next, we use an Image Processing technique to calculate the facial ratios from macro features like eyes, lips, etc. These facial ratios are calculated based on facial landmarks spread across the face. We use multiple facial ratios, so even if deformities occur in a part of the face it will not have much effect on the system rendering it immune to facial changes. Hence, this paper tries to close the gap in the previously devised facial matching methods which were not designed for deformed faces.
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U2 - 10.1007/978-981-16-6246-1_32
DO - 10.1007/978-981-16-6246-1_32
M3 - Conference contribution
AN - SCOPUS:85123314616
SN - 9789811662454
T3 - Lecture Notes in Networks and Systems
SP - 377
EP - 389
BT - Proceedings of 1st International Conference on Computational Electronics for Wireless Communications - ICCWC 2021
A2 - Rawat, Sanyog
A2 - Kumar, Arvind
A2 - Kumar, Pramod
A2 - Anguera, Jaume
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Conference on Computational Electronics for Wireless Communications, ICCWC 2021
Y2 - 11 June 2021 through 12 June 2021
ER -