Paper Title
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