2D(PC)2 A for Face Recognition with One Training Image per Person
Junbao Li, Shu‐Chuan Chu, Jeng‐Shyang Pan · 2007
In the real-world application of face recognition system, owing to the difficulties of collecting samples or storage space of systems, only one sample image per person is stored in the system, which is so-called one sample per person problem. In this paper, we propose a novel algorithm, called 2D(PC)2A, to solve this problem. The procedure of 2D(PC)2A can be divided into the three stages: 1) creating the combined image from the original image 2) performing 2DPCA on the combined images; 3) classifying a new face based on assembled matrix distance (AMD). Experiments implemented on two real datasets show that 2D(PC)2A method is an efficient and practical approach for face recognition.