Bag of Features vs Deep Neural Networks for Face Recognition
Eliza Rebeca Tomodan, Cătălin Daniel Căleanu · 2018
This paper proposes a comparative study of Bag of Features (BoF) and Deep Neural Networks (DNN) approaches for the problem of face recognition. For the latter approach we consider three pre-trained models, namely AlexNet, ResNet50 and GoogleNet provided through Caffe Model Zoo and use them as feature extractors. Although these models were trained on different datasets, e.g., ImageNet, bottom-most layers act like universal feature extractors thus it is possible to be employed for different classification tasks. In order to adapt the models to various face datasets requirements we performed modifications to the input data as well as to the output layer of the pre-trained models by replacing it with a multiclass SVM classifier.