A secure face recognition scheme using noisy images based on kernel sparse representation
Masakazu Furukawa, Yuichi Muraki, Masaaki Fujiyoshi, Hitoshi Kiya · 2013
This paper proposes a secure face recognition scheme based on kernel sparse representation where facial images are visually encrypted. In the proposed scheme, a noisy image is added to all facial images, including a query image, to protect facial images. Noise-added facial images are further clipped for preventing unauthorized noise removing. Thanks to these strategies, a leakage of facial images will not disclose users' privacy, even the noisy image is also leaked. That is, the proposed scheme protects users' privacy and does not need to manage the noisy image securely. The proposed scheme directly applies kernel sparse representation based face recognition to noisy facial images, viz., decryption-free. Experimental results demonstrate that the face recognition performance of kernel sparse representation is not degraded, even facial images are visually encrypted.