Discriminative Prototype Learning in Open Set Face Recognition

We address the problem of prototype design for open set face recognition (OSFR) using single sample image. Normalized Correlation (NC), also known as Cosine Distance, offers many benefits in accuracy and robustness compared to other distance measurement in OSFR problem. Inspired by classical Learning Vector Quantization (LVQ), a novel discriminative learning method is proposed to design a discriminative prototype used by NC classifier. Specifically, we develop an objective function that fixes the NC score between the prototype and within-class sample at a high level and minimizes the similarity between the prototype and between-class samples. Several experiments conducted on benchmark databases demonstrate the superior performance of the prototype designed compared to the original one.