We present a robust elastic and partial matching metric for face recognition. To handle challenges such as pose, facial expression and partial occlusion, we enable both elastic and partial matching by computing a part based face representation. In which N local image descriptors are extracted from densely sampled overlapping image patches. We then define a distance metric where each descriptor in one face is matched against its spatial neighborhood in the other face and the minimal distance is recorded. For implicit partial matching, the list of all minimal distances are sorted in ascending order and the distance at the αN-th position is picked up as the final distance. The parameter 0 ≤ α ≤ 1 controls how much occlusion, facial expression changes, or pixel degradations we would allow. The optimal parameter values of this new distance metric are extensively studied and identified with real-life photo collections. We also reveal that filtering the face image by a simple difference of Gaussian brings significant robustness to lighting variations and beats the more utilized self-quotient image. Extensive evaluations on face recognition benchmarks show that our method is leading or is competitive in performance when compared to state-of-the-art.
['Gang Hua', 'Amir Akbarzadeh']
A robust elastic and partial matching metric for face recognition