Person Re-Identification Using Texture Driven Deep Learning
Person re-identification largely involves input patterns or attributes. Recognizing a person across non-overlapping
camera views, with different pose, illumination, and camera characteristics. To propose to tackle this problem by training a
deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to
common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is
used to train a feature extraction network using pairs of similar and dissimilar images.
Keywords- person re-identification, deep learning, neural networks, feature embedding