  Person re-identification (PRe-ID) is a crucial task in security,
surveillance, and retail analysis, which involves identifying an individual
across multiple cameras and views. However, it is a challenging task due to
changes in illumination, background, and viewpoint. Efficient feature
extraction and metric learning algorithms are essential for a successful PRe-ID
system. This paper proposes a novel approach for PRe-ID, which combines a
Convolutional Neural Network (CNN) based feature extraction method with
Cross-view Quadratic Discriminant Analysis (XQDA) for metric learning.
Additionally, a matching algorithm that employs Mahalanobis distance and a
score normalization process to address inconsistencies between camera scores is
implemented. The proposed approach is tested on four challenging datasets,
including VIPeR, GRID, CUHK01, and PRID450S, and promising results are
obtained. For example, without normalization, the rank-20 rate accuracies of
the GRID, CUHK01, VIPeR and PRID450S datasets were 61.92%, 83.90%, 92.03%,
96.22%; however, after score normalization, they have increased to 64.64%,
89.30%, 92.78%, and 98.76%, respectively. Accordingly, the promising results on
four challenging datasets indicate the effectiveness of the proposed approach.
