Tied factor Analysis using Bagging for heterogeneous face recognition

Heterogeneous face recognition is a challenging research problem which involves matching of the faces captured from different sensors. Very few methods have been designed to solve this problem using intensity features and considered small sample size issue. In this paper, we consider the worst case scenario when there exists a single instance of an individual image in a gallery with normal modality i.e. visual while the probe is captured with alternate modality, e.g. Near Infrared. To solve this problem, we propose a technique inspired from tied factor Analysis (TFA) and Bagging. In the proposed method, the original TFA method is extended to handle small training samples problem in heterogeneous environment. But one can report the higher recognition rates by testing on small subset of images. Therefore, bagging is introduced to remove the effects of biased results from original TFA method. Experiments conducted on a challenging benchmark HFB and Biosecure face databases validate its effectiveness and superiority over other state-of-the-art methods using intensity features holistically

Tied factor Analysis using Bagging for heterogeneous face recognition

Heterogeneous face recognition is a challenging research problem which involves matching of the faces captured from different sensors. Very few methods have been designed to solve this problem using intensity features and considered small sample size issue. In this paper, we consider the worst case scenario when there exists a single instance of an individual image in a gallery with normal modality i.e. visual while the probe is captured with alternate modality, e.g. Near Infrared. To solve this problem, we propose a technique inspired from tied factor Analysis (TFA) and Bagging. In the proposed method, the original TFA method is extended to handle small training samples problem in heterogeneous environment. But one can report the higher recognition rates by testing on small subset of images. Therefore, bagging is introduced to remove the effects of biased results from original TFA method. Experiments conducted on a challenging benchmark HFB and Biosecure face databases validate its effectiveness and superiority over other state-of-the-art methods using intensity features holistically