基于局部正脸合成和两阶段表示的三阶段人脸识别算法

Three-Phase Face Recognition Algorithm via Locally Frontal Face Synthesis and Two-Phase Face Recognition

基于两阶段表示的人脸识别算法(TPTSR)识别率高,并且对遮挡、噪声等干扰鲁棒,但是当人脸姿态有较大变化时,TPTSR算法的识别率会明显下降.针对这一问题,提出基于局部正脸合成和TPTSR的三阶段人脸识别算法:第一个阶段,正脸合成阶段,利用提出的正脸合成算法和视点库,将偏转角度较大的测试样本合成相应的正脸,作为新的测试样本;第二个阶段,样本筛选阶段,选择出对最新的测试样本最具表示能力的M个训练样本;第三个阶段,决策识别阶段,用这M个训练样本做人脸识别.通过与经典算法的对比实验证明,提出的3PTSR人脸识别算法能有效解决多姿态人脸识别问题.

Two-phase test sample representation algorithm （TPTSR）, which is robust to interference such as occlusion and noise, performs well in face recognition without pose variation. However, its recognition rate will decline when the face pose varies dramatically. To solve this problem, a three-phase test sample representation algorithm was proposed. The first was frontal face synthesizing was a frontal face with small horizontal deflection angle was synthesized using view-library and proposed frontal face synthesizing algorithm. Thus, a frontal face was synthesized as the new test sample. The second was training sample selecting phase, M training samples that make the most contribution were selected to represent the new test sample. The third was decision and recognition phase, a face was recognized using the M training samples. Experiments on some publicly available face recognition benchmarks demonstrate that the proposed 3PTSR algorithm outperforms the state-of-the-art methods in challenging conditions, especially for the face with various poses.