2 research outputs found
A survey of face recognition techniques under occlusion
The limited capacity to recognize faces under occlusions is a long-standing
problem that presents a unique challenge for face recognition systems and even
for humans. The problem regarding occlusion is less covered by research when
compared to other challenges such as pose variation, different expressions,
etc. Nevertheless, occluded face recognition is imperative to exploit the full
potential of face recognition for real-world applications. In this paper, we
restrict the scope to occluded face recognition. First, we explore what the
occlusion problem is and what inherent difficulties can arise. As a part of
this review, we introduce face detection under occlusion, a preliminary step in
face recognition. Second, we present how existing face recognition methods cope
with the occlusion problem and classify them into three categories, which are
1) occlusion robust feature extraction approaches, 2) occlusion aware face
recognition approaches, and 3) occlusion recovery based face recognition
approaches. Furthermore, we analyze the motivations, innovations, pros and
cons, and the performance of representative approaches for comparison. Finally,
future challenges and method trends of occluded face recognition are thoroughly
discussed
A novel spatially confined non-negative matrix factorization for face recognition
In this paper, a novel method for facial representation called Spatially Confined Non-Negative Matrix Factorization
(SFNMF) is presented. SFNMF aims to extract more spatially confined, parts-based representation from the NMF based representation by merely removing non-prominent region, and focalize on the salient feature. SFNMF derived a significant set of basis which allows a non-subtractive representation of images and these bases manifest localized features. Experimental results are presented to compare SFNMF with NMF and Local NMF. Advantageous of SFNMF is demonstrated when SFNMF achieves highest verification rate among the other