DWT based Person Re-Identification using GAN

Abstract

The recent development in person re-identification has challenging task for variations in pose, illumination, expression, and also similar appearance between two different persons. In this paper, we propose Discrete Wavelet Transform (DWT) based person re-identification using Generative Adversarial Network (GAN). The CMU multi-PIE face database with multiple viewpoints and illuminations is considered to test the model. The profile side view face images to be tested are converted into frontal face images using Two-pathway generator adversarial network (TP-GAN). The frontal face images are loaded into the server to create server database. The synthesized TP-GAN images and server database images are pre-processed to convert RGB into grayscale images and also to convert into uniform face image dimensions. The person re-identification is based on feature extraction through DWT, which generates one low frequency LL band and three high frequency bands LH, HL and HH. The LL band coefficients are considered as final features, which are noise-free and compressed number of features. The features of profile side view images and server database images are compared using Normalized Euclidean Distance (NED) and threshold values for person re-identification.