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Deep Learning strategies for face recognition: Design and experimental comparison on benchmark problems


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  Dr V Schetinin  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

There are still problems in applications of Machine Learning for face recognition. Such factors as lighting conditions, head rotations, emotions, and view angles affect the recognition accuracy. A large number of recognition subjects requires complex class boundaries. Deep Neural Networks have provided efficient solutions, although their implementations require massive computations for evaluation and minimisation of error functions. Gradient algorithms provide iterative minimisation of the error function. The maximal performance is achieved if parameters of gradient algorithms and neural network structures are properly set. The use of pairwise neural network structures often improves the performance because such structures require a small set of optimisation parameters. The experiments have been conducted on some face biometric benchmark data sets, and the main findings are presented and discussed.

Research questions: (1) to explore the ability of Deep Learning strategies to extend the face recognition (2) to explore ways of designing a new approach within the multiclass and pairwise Deep Learning frameworks.

The deadlines are as follows:

For March starters:

International applicants - 30th November 2021

UK nationals - 18th January 2022

For October starters:

International applicants - 30th June 2022

UK nationals - 5th August 2022


References

Publications:
[1] Selitskaya N. et al. (2020) Deep Learning for Biometric Face Recognition: Experimental Study on Benchmark Data Sets. In: Jiang R., Li CT., Crookes D., Meng W., Rosenberger C. (eds) Deep Biometrics. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-32583-1_5
[2] Uglov, J., Jakaite, L., Schetinin, V. et al. Comparing Robustness of Pairwise and Multiclass Neural-Network Systems for Face Recognition. EURASIP J. Adv. Signal Process. 2008, 468693 (2007). https://doi.org/10.1155/2008/468693
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