Face Recognition in surveillance conditions with Bag-of-Words, using unsupervised Domain Adaptation

Face Recognition (FR) in surveillance scenarios has attracted the attention of researchers over the last few years. The bottleneck as a large gap in both resolution and contrast between training (high-resolution gallery) and testing (degraded, low quality probes) sets, must be overcome using efficient statistical learning methods. In this paper, we propose a Bag-of-Words (BOW) based approach for face recognition combined with Domain Adaptation (DA), to overcome this challenging task of FR in degraded conditions. The dictionary of BOW is formed using dense-SIFT features, using an adaptive spatially varying density. The sampling of the keypoints is denser in the discriminative parts of the face, while it is loosely sampled at some less-interesting (pre-decided) zones of the face. FR using BOW-based face representation is made more efficient using an unsupervised method of DA. Proposed method of DA considers the training set to be the source and the test set to be the target domains. Transformation from source to target is estimated using eigen-analysis of the BOW-based features, which is the novelty and contribution of our proposed work on FR for surveillance applications. Results on the two-real world surveillance face datasets shows the efficiency of the proposed method using ROC and CMC measures.