Learning a non-linear combination of Mahalanobis distances using statistical inference for similarity measure

• Published in: IET computer vision 2015-08, Vol.9 (4), p.541-548
• Main Authors: Mostafa, Eslam, Ali, Asem M, Farag, Aly A
• Format: Article
• Language: English
• Subjects: capturing process distribution; Computer vision; computer vision application; Face recognition; face verification; feature extraction; feature vector representation; image capture; image representation; image sampling; inference mechanisms; interclass sample; intraclass sample; Learning; learning (artificial intelligence); LFW; likelihood ratio test; Mahalanobis distance nonlinear combination; person identification; Samples; Similarity; Statistical analysis; statistical inference; Statistical methods
• ISSN: 1751-9632; 1751-9640; 1751-9640
• DOI: 10.1049/iet-cvi.2014.0011

In this study, the authors learn a similarity measure that discriminates between inter-class and intra-class samples based on a statistical inference perspective. A non-linear combination of Mahalanobis is proposed to reflect the properties of a likelihood ratio test. Since an object's appearance is influenced by the identity of the object and variations in the capturing process, the authors represent the feature vector, which is the difference between two samples in the differences space, as a sample that is drawn from a mixture of many distributions. This mixture consists of the identities distribution and other distributions of the variations in the capturing process, in case of dissimilar samples. However, in the case of similar samples, the mixture consists of the variations in the capturing process distributions only. Using this representation, the proposed similarity measure accurately discriminates between inter-class and intra-class samples. To highlight the good performance of the proposed similarity measure, it is tested on different computer vision applications: face verification and person re-identification. To illustrate how the proposed learning method can easily be used on large scale datasets, experiments are conducted on different challenging datasets: labelled faces in the wild (LFW), public figures face database, ETHZ and VIPeR. Moreover, in these experiments, the authors evaluate different stages, for example, features detector, descriptor type and descriptor dimension, which constitute the face verification pipeline. The experimental results confirm that the learning method outperforms the state-of-the-art.