Jenkins R, Burton A M
Department of Psychology, University of Glasgow, Glasgow G12 8QQ, UK.
Science. 2008 Jan 25;319(5862):435. doi: 10.1126/science.1149656.
Accurate face recognition is critical for many security applications. Current automatic face-recognition systems are defeated by natural changes in lighting and pose, which often affect face images more profoundly than changes in identity. The only system that can reliably cope with such variability is a human observer who is familiar with the faces concerned. We modeled human familiarity by using image averaging to derive stable face representations from naturally varying photographs. This simple procedure increased the accuracy of an industry standard face-recognition algorithm from 54% to 100%, bringing the robust performance of a familiar human to an automated system.
精确的人脸识别对于许多安全应用至关重要。当前的自动人脸识别系统会被光照和姿态的自然变化所击败，这些变化对人脸图像的影响往往比对身份变化的影响更为深远。唯一能够可靠应对这种变化的系统是熟悉相关人脸的人类观察者。我们通过图像平均法来模拟人类的熟悉度，以便从自然变化的照片中获得稳定的人脸表征。这个简单的过程将一种行业标准人脸识别算法的准确率从54%提高到了100%，使自动化系统具备了熟悉人类的强大性能。
