VGGFace Transfer Learning and Siamese Network for Face Recognition

Traditionally, data mining algorithms and machine learning algorithms are engineered to approach the problems in isolation. These algorithms are employed to train the model in separation on a specific feature space and same distribution. Depending on the business case, a model is trained by applying a machine learning algorithm for a specific task. A widespread assumption in the field of machine learning is that training data and test data must have identical feature spaces with the underlying distribution. On the contrary, in real world this assumption may not hold and thus models need to be rebuilt from the scratch if features and distribution changes. It is an arduous process to collect related training data and rebuild the models. In such cases, Transferring of Knowledge or transfer learning from disparate domains would be desirable. Transfer learning is a method of reusing a pre trained model knowledge for another task. Transfer learning can be used for classification, regression and clustering problems. This paper uses one of the pre trained models – VGGFace with Deep Convolutional Neural Network to classify images. Aafaq Altaf | Rahee Jan | Andleeb Qadir | Shahid Mohid-ud-din Bhat "VGGFace Transfer Learning and Siamese Network for Face Recognition" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47869.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/47869/vggface-transfer-learning-and-siamese-network-for-face-recognition/aafaq-altaf