Abstract
Recognition of facial aging using face images has enormous applications in forensic and security control. In this study, an attempted to verify the face images of adolescence and adults by using a single reference sample have been made. To study this problem, we have designed our model by considering with-patch based and without-patch based face images. Both local and pre-trained deep features have been extracted during feature extraction. Further, the well-known subspace techniques such as Principal Component Analysis (PCA) and Fisher Linear Discriminant (FLD) have been adopted for dimensionality reduction. To measure the model’s goodness, we have created our dataset consisting of two images of 64 persons, each with an age gap of 10 years between adolescents (15 years old) and adults (25 years old). The comparative analysis between with-patch and without-patch based images, and PCA and FLD have been studied effectively. The pre-trained networks give the highest matching characteristics at 96% using top projection vectors. The experimental results reveal that the deep learning model outperforms the state-of-the-art method for face verification. Therefore, the obtained results are promising and encouraging for face biometric applications.
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Sumithra, R., Guru, D.S., Aradhya, V.N.M., Raghavendra, A. (2021). Face Verification Using Single Sample in Adolescence. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_30
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DOI: https://doi.org/10.1007/978-981-16-1092-9_30
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