  Person Re-identification is defined as a recognizing process where the person
is observed by non-overlapping cameras at different places. In the last decade,
the rise in the applications and importance of Person Re-identification for
surveillance systems popularized this subject in different areas of computer
vision. Person Re-identification is faced with challenges such as low
resolution, varying poses, illumination, background clutter, and occlusion,
which could affect the result of recognizing process. The present paper aims to
improve Person Re-identification using transfer learning and application of
verification loss function within the framework of Siamese network. The Siamese
network receives image pairs as inputs and extract their features via a
pre-trained model. EfficientNet was employed to obtain discriminative features
and reduce the demands for data. The advantages of verification loss were used
in the network learning. Experiments showed that the proposed model performs
better than state-of-the-art methods on the CUHK01 dataset. For example, rank5
accuracies are 95.2% (+5.7) for the CUHK01 datasets. It also achieved an
acceptable percentage in Rank 1. Because of the small size of the pre-trained
model parameters, learning speeds up and there will be a need for less hardware
and data.
